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Neurological Disorders of Attention

Abstract and Keywords

Attention deficits are a frequent and particularly disabling consequence of many neurological disorders, from patients with focal brain lesions through to individuals with traumatic brain injury or neurodegenerative conditions, such as Parkinson’s disease. They are often associated with apparent confusion, fatigue, irritability, and increased time and effort to perform even simple everyday tasks, and constitute a real challenge for rehabilitation. In many cases, attention deficits may be crucial factors underlying failures of memory and higher cognitive functions, contributing to difficulties in resuming previous activities and independent daily living. Here the authors first consider four aspects of attention—selective, sustained, executive, and divided—together with brain regions and networks considered to underpin normal attention and disorders of attention. The authors focus on focal brain lesions, traumatic brain injury and Parkinson’s disease as important examples illustrating the effects of different brain pathologies on attention function.

Keywords: focal brain lesions, traumatic brain injury, Parkinson’s disease, attention networks


It is now relatively well accepted that attention is not a unitary process but rather can be fractionated into subcomponents such as selective, sustained, divided, and executive attention (Leclercq and Zimmermann 2002; Petersen and Posner 2012). The identification of specific disruptions in these different subcomponents is necessary to indicate appropriate treatment for neurological conditions with attentional deficits. Many models have been proposed to describe these attentional components and their underlying neural structure. These were largely driven by lesion studies and, later, by functional imaging (Desimone and Duncan 1995; Corbetta and Shulman 2002; Husain and Rorden 2003; Posner 2003). Although they have played an important role in the evolution of modern clinical neuroscience, a potential limitation of these previous ‘localizationist’ approaches is that they often consider the function of one particular brain region in isolation from the rest of the brain. However, the emergence of new theories of the brain as being organized into large-scale networks proposes that the transfer of information between distinct regions also plays a critical role in efficient attentional processing (Mesulam 1981, 1990; Corbetta and Shulman 2002; Posner and Rothbart 2007).

Consequently, in principle, there are three main mechanisms by which a neurological disorder can affect the function of these networks: either via direct damage to network ‘nodes’ (e.g. focal lesions or neurodegenerative conditions), damage to the network connections (e.g. axonal injury in traumatic head injury, multiple sclerosis, and neurodegenerative conditions), and/or dysfunction of neurotransmitter systems supporting the function of these networks. This chapter aims to provide an overview of how such (p. 1029) mechanisms might be disrupted in neurological conditions and the impact they can have in patients. We focus on three key disorders which serve as important models for understanding attention deficits across a range of conditions: focal brain lesions (other than those causing neglect or extinction which are reviewed by Vallar and Bolognini (chapter 33, this volume), traumatic brain injury, and Parkinson’s disease. First, we briefly introduce four attentional processes around which this chapter is structured, and consider some of the brain regions and neural networks that have been proposed to underpin them. Characterization of such structure–function correlations is in its infancy and by no means established. Here, we present the reader with emerging views regarding the functions of attention networks and their disruption in brain disorders, but would urge caution since these concepts are likely to evolve considerably over the next few years.

Selective attention

Selective or focused attention refers to the ability to attend selectively to information that is relevant to a task while ignoring irrelevant and distracting information. Selective attention deficits might be evident when a response produced by automatic processing interferes with a response produced by controlled processing. For example, Posner’s covert orienting of attention task (Posner 1980) has been used extensively to investigate discrete attentional deficits in a number of neurological disorders. In this paradigm, healthy subjects typically show a benefit in reaction time (RT) to targets appearing at a validly cued location, and an RT cost when targets appear at an unexpected location. This suggests that attention can be captured by a cue so that visual processing is selectively oriented towards it, thereby improving performance if a target subsequently appears at that location. Other important probes of selective attention include measures of visual search (Mack and Eckstein 2011; Davis and Palmer 2004) and attentional dwell time (Dux and Marois 2009).

Neurological Disorders of AttentionClick to view larger

Figure 34.1 Four brain networks involved in different aspect of attentions. Core regions of the dorsal attention network include regions of the dorsal frontal cortex, along the precentral sulcus, close or at the frontal eye field, and regions of the dorsal parietal cortex, particularly the intraparietal sulcus and superior parietal lobule. Core regions of the ventral attention network include the right inferior frontal gyrus and the right temporoparietal junction. The Salience network comprises a set of paralimbic structures, most prominently the anterior cingulate cortex, the pre-supplementary motor area and bilateral orbitofrontal insula. The two major nodes of the default mode network (DMN) are the precuneus/posterior cingulate cortex and the ventro-medial prefrontal cortex. The DMN comprises other regions not represented on this figure such as the inferior parietal lobes and the parahippocampal gyri.

Previous neuroimaging and lesion studies consistently identified a bilateral network of fronto-parietal regions as supporting this type of selective attention (Gitelman et al. 1999; Nobre et al. 1997; Nobre 2001; Yantis and Serences 2003; Hopfinger et al. 2001; Kastner and Ungerleider 2000). Core regions of this so-called ‘orienting’ or ‘dorsal attention’ network (DAN) include parts of the dorsal frontal cortex, along the precentral sulcus, close or at the frontal eye field (FEF), and regions of the dorsal parietal cortex, particularly the intraparietal sulcus (IPS) and superior parietal lobule (Fig. 34.1). It has been proposed that a primary function of this network is to generate and maintain endogenous (‘top-down’) signals based on current goals and pre-existing information about likely future events. The selection of information that guides attention may also occur in a ‘bottom-up’ fashion, that is, driven by competition between sensory inputs. Corbetta and Shulman (2002) proposed that such stimulus-driven attention is supported by a right lateralized, ventral attentional network (VAN) comprising regions of the right inferior frontal cortex and the temporo-parietal junction (Fig. 34.1). (p. 1030) According to their proposal, this network would serve as an alerting system that acts as a circuit breaker of ongoing cognitive activity when salient, unexpected, or low frequency stimuli are detected.

Sustained attention

Sustained attention refers to the ability to maintain attention on task requirements. Some authors have distinguished this function from ‘phasic alertness’: processes underlying the ability to improve performance following a warning signal, for example an auditory tone or visual cue (Posner 2008). Deficits in sustained attention can be considered to comprise two distinct components: vigilance level and vigilance decrement over time (Sarter, Givens, and Bruno 2001). A decrease in vigilance level leads to lapses in attention, often associated with momentary fluctuations in RT or response errors (Robertson et al. 1997) which can be indexed by intra-individual variability (IIV). Vigilance tasks typically require the participant to monitor a series of stimuli in order to detect infrequent targets. A vigilance decrement corresponds to the inability to maintain attention over a prolonged period of time, and is characterized by increased RT and/or error rate with time on task (Mackworth 1948). In general, such decrements in performance have been more frequently observed under conditions of high cognitive load or attentional demand, such as when stimuli are presented at a high event rate (for a review, see Sarter et al. 2001). (p. 1031)

Previous lesions and neuroimaging studies have consistently documented a critical role for a right hemispheric ventral fronto-parietal system in sustaining attention (Wilkins, Shallice, and McCarthy 1987; Pardo, Fox, and Raichle 1991; Coull, Frackowiak, and Frith 1998; Posner and Rothbart 2007; Robertson 2001; Husain and Rorden 2003; Singh-Curry and Husain 2009). Thus the VAN appears to be involved in both stimulus-driven attention and sustained attention, potentially allowing functional interaction between stimulus-driven attention and internally maintained sustained attention (Coull 1998). Consistent with this proposal, deactivation observed in the VAN as vigilance decreases over time is opposed by the demand to respond to intermittent target objects (Coull et al. 1998). Furthermore, exogenous stimuli have been found to activate and improve vigilant attention (Manly et al. 2002; O’Connor et al. 2004), demonstrating that stimulus-driven attention can modulate cortical systems supporting sustained attention.

Recent neuroimaging and electrophysiological studies suggest that the right VAN might not be the only system involved in sustained attention. Indeed, it has recently become apparent that brain activity within the default mode network (DMN) also tracks fluctuations in attention (Weissman et al. 2006; Sonuga-Barke and Castellanos 2007; Hayden, Smith, and Platt 2009) (Fig. 34.1). In contrast to the right lateralized fronto-parietal VAN system, the DMN shows a reduction in activation during externally oriented attentionally demanding tasks (Raichle et al. 2001). This anticorrelation has been proposed to reflect the dichotomy between tasks requiring internally oriented versus externally oriented attentional modes (Fransson 2005). As the attention demands of a cognitive task increase, this dichotomy becomes more pronounced, and the strength of the anticorrelation has been found to be associated with increased vigilance level (Kelly et al. 2008). It has been proposed that increasing attention may be accomplished either by predominantly boosting fronto-parietal network activity (i.e. regions of the DAN and VAN), deactivating the DMN, or a combination of the two (Lawrence et al. 2003).

Executive control of attention

Current views of ‘executive control’ generally refer to cognitive processes that allow the production of adaptive and flexible behaviour, such as monitoring for situations where automatic actions need to be suppressed or changed, inhibiting or changing those actions, monitoring performance outcome and adjusting behaviour when needed. They stem from concepts that have emerged from Norman and Shallice’s model of a ‘supervisory attention system’ (Norman and Shallice 1986). Executive control of attention has often been studied using paradigms that involve conflict, such as the Stroop task in which conflict or interference occurs when the colour word name differs from the colour of the ink. In the modern literature, executive control is also sometimes referred to as ‘cognitive control’.

Inhibitory control, defined as the ability to suppress inappropriate or no longer required responses, is another important aspect of executive attention that is frequently impaired (p. 1032) in many neurological disorders. It has been extensively studied both in healthy volunteers and clinical populations using the stop-signal task (Logan, Cowan, and Davis 1984). This task is based on a simple choice reaction-time task, but at irregular intervals and unpredictably for the participants, the Go stimulus is followed by a stop signal (e.g. flashing visual shape), which instructs subjects to withhold their response. The time it takes for a subject to inhibit a response can be estimated by the stop-signal reaction time (SSRT).

While it is clear that regions of the frontal lobes are strongly involved in executive control of attention, the way dorsal and ventral frontal areas interact with each other, as well as with more posterior regions, remains less well understood. There is a long history of lesion and imaging research that has implicated dorsolateral prefrontal cortex (DLPFC) in executive control, such as maintaining task sets and switching flexibly to new task sets when required (Shallice 1988; Fuster 1997). More recently, medial frontal regions such as the anterior cingulate cortex (ACC) and the pre-supplementary motor area (pre-SMA) have been implicated in conflict detection, error monitoring, and inhibition or change of motor plans (Botvinick et al. 2001; Garavan et al. 2003; Nachev et al. 2007; Sharp et al. 2010; Rushworth 2008; Rushworth et al. 2007). The ACC often co-activates with insular cortex across a variety of tasks (Dosenbach et al. 2006). These regions are both functionally and structurally connected (Seeley et al. 2007; van den Heuvel et al. 2009) and have been referred to as the ‘Core’ or ‘Salience’ network (Fig. 34.1) which is anatomically distinct from the VAN or DAN. It has been proposed that activity within this network is not task-specific but rather salience-driven, regardless of whether such salience is cognitive, emotional, or homeostatic (Seeley et al. 2007), providing stable ‘set-maintenance’ over entire task periods (Dosenbach et al. 2006, 2008).

Divided attention

Divided attention refers to the ability to process simultaneously more than one source of information at a time. This aspect of attention is directly linked to the concept of attentional resource, which assumes that attention processing capacity is limited (Kahneman 1973). As a consequence, allocating additional resources to one task can improve performance on that task, but it depletes attentional resources available for other concurrent tasks (Luck et al. 1996). Divided attention deficits have often been studied using dual-task paradigms, under conditions of high cognitive load. The observation that simultaneously performing two well learnt, relatively automatic tasks (with minimal demand on executive attention) does not generally lead to performance impairment suggests that impairments in divided attention might reflect a limitation of executive attention resources. Few studies have attempted to map divided attention onto a specific brain system. In general, fMRI studies have shown that divided attention is associated with increased recruitment of brain networks supporting selective and executive attention (Hahn et al. 2008; Loose et al. 2003; Vohn et al. 2007), potentially reflecting demand on these systems to selectively process information from two different tasks, implement rules and select appropriate responses.

(p. 1033) Attention Deficits after Focal Brain Lesions


Focal lesions that cause deficits of attention provide an important opportunity to study the role of brain regions in directing attention. Although some of the most striking effects are observed in the syndromes of unilateral neglect or extinction (reviewed in Vallar and Bolognini (chapter 33), this volume), many other deficits in several different aspects of attention occur following focal brain injury. Below we review some key findings, excluding investigations of patients who suffer from neglect or extinction. Many of these studies focus on parietal and frontal regions which are part of the DAN, VAN, or Salience network that have been implicated in imaging studies of attention in healthy individuals (Corbetta and Shulman 2002; Singh-Curry and Husain 2009).

Selective attention

Mild lateralized effects in selective attention, worse to the contralesional side, are common following unilateral brain lesions. In fact, in the classical study of Posner and his colleagues, often cited as demonstrating in parietal neglect patients a deficit in disengaging and shifting attention contralesionally, five of the thirteen cases had no demonstrable signs of visual extinction or neglect (Posner et al. 1984). The key finding in that study was that parietal lesions can lead to a directional deficit in deploying attention from an invalidly cued location on the Posner exogenous orienting task. By contrast, thalamic lesions lead to difficulty in engaging attention to the contralesional side, resulting in slow RTs for targets on the side contralateral to the lesion, regardless of whether attention is pre-cued to that location (Rafal and Posner 1987).

The precise location within parietal cortex where damage leads to selective attention deficits has been the subject of some debate (reviewed in Vandenberghe, Molenberghs, and Gillebert 2012). Recent studies of patients with extremely focal lesions involving the IPS and superior parietal lobe (SPL) suggest these regions play a critical role, consistent with functional imaging results from healthy individuals. Gillebert et al. (2011) found that their patient with damage to the left posterior IPS showed a deficit only for invalidly (centrally) cued contralesional targets, an effect that was amplified when an irrelevant, ipsilesional distractor was presented in competition with a valid contralesional target. By contrast, a patient with a very small lesion of the right middle IPS, extending into the SPL, demonstrated a bilateral impairment on invalidly cued trials. These effects have been interpreted in terms of possible different roles of posterior and middle IPS in compiling a ‘priority map’ of items in visual space (Vandenberghe et al. 2012; see also Bisley and Goldberg 2010). (p. 1034)

Patients with prefrontal lesions also show selective attention deficits, missing more items and showing increased RTs to detected targets presented contralesionally (Barceló, Suwazono, and Knight 2000). Using a whole-report paradigm and analysis based on Bundesen’s Theory of Visual Attention (in chapter 37, this volume), Habekost and Bundesen (2003) have reported slowing of attention for left-sided stimuli (as measured by a lower capacity of entry into working memory) following right frontal damage. Further studies, conducted on a group of patients with focal lesions using this methodology, revealed that parietal damage, particularly involving the temporal-parietal junction (TPJ), reduced processing speed and visual short-term memory capacity (Peers et al. 2005). By contrast, measures of attention weighting (directional bias and ‘top-down’ filtering ability) were best predicted by total volume of lesion—not simply frontal involvement. These results correspond well to the findings using the Posner task, demonstrating that directional biases in deploying attention are not confined to patients with clinical neglect or extinction (Posner et al. 1984).

In addition to directional biases in attention, prefrontal lesions can also produce bilateral deficits as demonstrated with endogenous cueing. Vecera and Rizzo (2004) used a central cueing task to show that a frontal lesion impaired voluntary attention shifts, but with preserved exogenous orienting. Similarly, frontal lobe resection impaired the ability to utilize informative spatial pre-cues, presented in the form of arrows pointing to the most likely location of an upcoming target (Koski, Paus, and Petrides 1998). These findings are supported by the results of electrophysiological studies that show frontal lesions attenuate neural correlates of attention in both auditory and visual domains. For example, Woods and Knight (1986) reported that patients with left prefrontal lesions lack ERP evidence of attentional selection in dichotic listening, while Barceló et al. (2000) reported that even early visual processing in extrastriate regions (<125 ms) is reduced by prefrontal damage.

Some authors have also investigated the effects of focal lesions on global vs. local attention, or the ability to shift attention from a wide to a tight attention focus. These studies have often used Navon figures: large, ‘global’ letters made up of small, ‘local’ letters (Navon 1977). In normal healthy people, RTs are generally faster in the globally directed condition than the local one (Navon 1977). In addition, RTs to the local level are longer—demonstrating interference—when the letters at the two levels are different (e.g. local ‘S’s forming a global ‘H’) compared to when they were the same (e.g. local ‘S’s forming a global ‘S’). In contrast, patients with posterior lesions centred on the posterior superior temporal gyrus and adjacent caudal inferior parietal lobe showed no interference (Lamb, Robertson, and Knight 1989), suggesting these regions normally play a role in integration of and/or attention to local- and global-level information. Left and right hemisphere lesions appeared to affect attention deployment differentially, with a local advantage following right TPJ lesions and a global advantage following left superior temporal gyrus lesions (Robertson, Lamb, and Knight 1988). Other investigators have observed that patients with ventral lesions to extrastriate cortex have a global bias for Navon figures, whereas dorsal lesions lead to a local bias (Riddoch et al. 2008). (p. 1035)

Müller-Plath and colleagues (2010) have developed an attractive method to study neuropsychological effects of attention. Their search task varies set-size and target-distractor similarity independently, and using a model of serial vs. parallel visual search, they decomposed performance into three components: size of attention focus, dwell time, and movement time. In patients with unilateral focal lesions, these authors reported that damage to the DLPFC reduced the focus of attention while temporal lesions enlarged it, consistent with studies using Navon figures. Lesions involving the FEF, SPL, and parieto-occipital cortex significantly increased attention movement time, consistent with views of the SPL being involved in shifting attention (see Vandenberghe et al. 2012). Attention dwell time was significantly reduced in patients with damage to the anterior insula as well as the SPL.

Sustained attention

Several studies have implicated right inferior frontal regions—typically involving frontal regions of the VAN—in playing a key role in sustaining attention over time. Wilkins et al. (1987) first noted that right frontal patients were impaired at counting stimuli—auditory or tactile—when presented slowly (1 item/second) but not quickly (7/second). Clearly, such a deficit might be interpreted in many ways, including distractibility, memory interference, alertness, motivation, or fatigue. Subsequent studies have tried to narrow the interpretation. Godefroy et al. (1994) demonstrated that neither fatigability, nor practice or motivation was specifically worse in frontal patients.

The Sustained Attention to Response Task (SART) is similar to the simple reaction-time studies mentioned above, but also requires selectivity: responding to pre-specified target items and withholding responses to other, distracting items. Frontal patients demonstrate increased commission errors on the SART (Robertson et al. 1997). A voxelwise lesion analysis of 41 patients on SART demonstrated that commission errors correlate strongly with right inferior frontal gyrus (RIFG) damage (Molenberghs et al. 2009). Reduced post-error slowing correlated with right inferior frontal sulcus damage, comparable with findings in the go/no-go task (see below). Right prefrontal patients also show deficits on versions of the continuous performance task (CPT) with worsening effects as target complexity increased (Glosser and Goodglass 1990; Rueckert and Grafman 1996; Wilkins et al. 1987; Woods and Knight 1986), as well as demonstrating a vigilance decrement, performing slower with time on task (Rueckert and Grafman 1996; Wilkins et al. 1987).

Frontal patients also demonstrate slowing (Howes and Boller 1975; Rueckert and Grafman 1996) and increased variability (Picton et al. 2006) of simple RTs. With respect to alerting, Alivisatos and Milner used a warning-signal task to show that right and left frontal patients are unable to utilize a ‘get-ready’ signal to speed subsequent responses (Alivisatos and Milner 1989). In fact, frontal patients may show a reversed foreperiod effect, slowing down with longer delay periods (Stuss et al. 2005). (p. 1036)

Although most studies have implicated right inferior frontal regions, there is also some evidence for a role of right dorsomedial regions (also part of the Salience network), with some investigators arguing for a role of these areas in energizing attention for responses (Alexander et al. 2005). Lesions here lead to progressive slowing of response times. Regardless of the interpretation, lesion data confirm the key role played by the right inferior and dorsomedial frontal cortices in sustaining attention over time.

Divided and executive attention

Divided attention has perhaps been probed most simply by comparing RTs in a task with only one stimulus modality versus two possible modalities (Godefroy and Rousseaux 1996). Patients with lesions involving the left prefrontal cortex and head of the caudate were unduly slow on the dual-modality task. Further evidence pointing to a deficit in dividing attention in prefrontal patients comes also from bedside tasks such as the trail-making test. In Part B of this test, participants have to search in alternating fashion for letters and numbers, joining them up as they find them. Thus this test also potentially assesses the ability to switch tasks and might not be simply a measure of divided attention. Patients with dorsolateral prefrontal lesions of either hemisphere and paramedian thalamic damage are most likely to be impaired, while those with inferomedial thalamic damage seem not to be (Stuss et al. 1988, 2001). Similarly, on multi-target visual search tasks, patients with frontal damage are specifically slowed when trying to find more than one type of target among distractors compared to searching for a single target (Richer et al. 1993).

In clinical studies, the Wisconsin Card-Sorting Test (WCST) is one of the most common tests used for executive attention in patients, but clearly there might be several reasons why patients might fail on this task, including keeping in mind task rules and previous outcomes. Damage to many areas impairs performance, including basal ganglia (Eslinger and Grattan 1993), DLPFC (Demakis 2003), thalamus, and even cerebellum, according to some authors (Mukhopadhyay et al. 2008). Patients with frontal lesions have deficits on the related stimulus-classification task from the CANTAB battery, specifically when switching to a previously irrelevant dimension (Owen et al. 1991). However, a review of 25 lesion studies concluded that although the WCST is a sensitive test of frontal lobe damage, it is by no means specific (Alvarez and Emory 2006). A recent lesion analysis found only a very mild effect of lesion location on task performance, with moderate localization to left prefrontal areas (Jodzio and Biechowska 2010). Other studies comparing subregions of the right frontal lobe have found no evidence of specificity of localization within the frontal lobe (Alvarez and Emory 2006; Davidson et al. 2007).

Problems in inhibition are characteristic of frontal lesions, including bedside tests of delayed alternation and interference (Roca et al. 2010). Right IFG lesions have been particularly implicated, with increased commission errors on the stop-signal task (Aron et al. 2003) and go/no-go task (Picton et al. 2007). In addition, some authors have presented evidence for a role of right dorsomedial areas in response inhibition (Floden and Stuss 2006; Picton et al. 2007), consistent with the view that the pre-SMA and right IFG form critical nodes of a stopping network (Aron et al. 2007). (p. 1037)

Frontal lesions also lead to deficits on tasks that produce stimulus or response conflict such as the Stroop or Eriksen flanker (Swick and Turken 2002; Ullsperger and von Cramon 2006; Coulthard, Nachev, and Husain 2008). Some authors who have reviewed the lesion evidence for localization of Stroop deficits conclude that lateral and dorsomedial prefrontal, but not orbitofrontal damage, is associated with performance impairments (Alvarez and Emory 2006). However, the results are inconsistent and, while some studies find no differences between lesion locations, others have reported quite specific differences between lesions to different frontal areas. For example, damage to the left ventrolateral region produced an increased number of incorrect responses to distractors, while right dorsomedial lesions (including ACC, SMA, and pre-SMA) and dorsolateral prefrontal areas, were associated with a slow RT and a decreased number of correct responses to targets on the Stroop (Alexander et al. 2007). Impaired Stroop performance has also been associated with thalamic lesions (Annoni et al. 2003; Ghika-Schmid and Bogousslavsky 2000).

This brief review of the effects of focal brain lesions on attention functions demonstrates how diverse findings might be organized in terms of a framework of selective, sustained, divided, and executive attention. Next, we consider an important example of brain damage that often has its greatest impact on the connections between brain regions that serve key roles in directing attention.

Attentional Deficits after Traumatic Brain Injury (TBI)


TBI produces a complex combination of focal lesions and traumatic axonal injury (Gentry, Godersky, and Thompson 1988) both of which can have an important impact on attention (for reviews on attention deficits after TBI, see Niemann, Ruff, and Kramer 1996; Cossa and Fabiani 1999; Chan 2001; Mathias and Wheaton 2007). While the way focal lesions affect these processes via, for example, direct damage to the frontal lobes, has been extensively described (for a review see Stuss and Knight 2002; Stuss 2011), the impact of traumatic axonal injury remains less clear. One key reason for focusing on TBI in this review is that this condition can provide important information on attention deficits due to disruption of white matter pathways in brains that usually had no pre-existing neuropathology.

Traumatic axonal injury is the most common pathological feature of TBI, found in almost three quarters of patients with moderate to severe injury (Smith, Meaney, and Shull 2003; Skandsen et al. 2010). However, it is likely to have been underestimated until recently, due to the lack of imaging techniques sensitive to identify axonal injury (for a review on techniques to characterize axonal injury see Sharp and Ham 2011). Diffusion tensor imaging (DTI) is a relatively recent MRI modality that provides a particularly (p. 1038) useful way to help understand TBI white matter pathology (Niogi and Mukherjee 2010; Zappalà, Thiebaut de Schotten, and Eslinger 2012). Frontal and temporal white matter structures (e.g. anterior corona radiata, uncinate fasciculus, superior longitudinal fasciculus, and fronto-occipital fasciculus), midline structures such as the corpus callosum and cingulum bundles, as well as cortical–subcortical connections, are the most frequently damaged (Rutgers et al. 2008; Niogi and Mukherjee 2010). White matter damage predicts functional outcome better than the presence, location, or volume of focal lesions (Benson et al. 2007; Sidaros et al. 2008; Kinnunen et al. 2011). It is observable even in patients with no visible focal lesions or microbleeds, and correlates with TBI severity (Nakayama et al. 2006; Kinnunen et al. 2011) and cognitive impairment following TBI (Salmond et al. 2006; Kraus et al. 2007; Niogi et al. 2008b; Little et al. 2010; Kinnunen et al. 2011).

Selective attention

On Posner’s covert orienting of attention task, a normal cost but reduced or absent benefit in RT to targets at expected locations has been observed both in acute and chronic moderate to severe TBI patients, suggesting an impairment in the ability to pre-engage attention to a cued location (Cremona-Meteyard et al. 1992; Cremona-Meteyard and Geffen 1994). However, this result was not replicated in a later study investigating a larger cohort of patients with severe TBI (Bate, Mathias, and Crawford 2001). More recently, Halterman et al. (2006) have reported findings using the Attentional Network Test, which attempts to separate three components of attention described by Posner, namely alerting, orienting, and executive control in the face of conflict (Fan et al. 2002). The results demonstrated that orienting and executive attention were impaired two days post-TBI, but only deficits in executive attention remained after a month (Halterman et al. 2006). In general, there does not seem to be strong evidence for persistent selective attention deficits after TBI. Rather, Stuss et al. (1989) suggested that TBI patients may have a relatively intact ability to focus attention, but that this might be achieved at a cost and could not be maintained by all patients, possibly as a result of limited available attention resources.

The frontal and parietal areas implicated in selective and sustained attention are richly interconnected via fibre tracts passing through the superior longitudinal fasciculus (SLF) (Schmahmann and Pandya 2006). Structural integrity of the SLF has been related to attention performance, both in healthy people (Bennett et al. 2012), and stroke patients with neglect (He et al. 2007; Doricchi et al. 2008; Bartolomeo, Thiebaut de Schotten, and Doricchi 2007). Although some studies have reported damage within the SLF after TBI (Messe et al. 2011; Kraus et al. 2007; Bendlin et al. 2008), this is not a consistent finding (Bonnelle et al. 2012) (Fig. 34.2: panels a and b (4) and (5)).

Other white matter tracts are also likely to be important for selective attention, but have not been extensively investigated in TBI. For example, a recent study in normal subjects using the Attentional Network Task (ANT) related orienting of attention performance to structural integrity of the splenium of the corpus callosum (Niogi et al. (p. 1039)

Neurological Disorders of AttentionClick to view larger

Figure 34.2 Comparison of white matter structure between TBI patients (N=57) and controls (N=25) in key white matter tracts. (a) White matter tracts between local maxima of brain activation (red spheres) or deactivation (blue spheres) during response inhibition represented in 3D on the MNI-152 T1 1mm brain 3D template. Tracts are shown for the connections between (1) the right anterior insula (rAI) and the anterior cingulate cortex (ACC) (Salience network), (2) the precuneus (Precu) and medial prefrontal cortex (mPFC) bilaterally (Default mode network), (3) the right pre-supplementary motor area (pre-SMA) and right inferior frontal gyru (IFG), (4) the right IFG and the right temporo-parietal junction (TPJ) (VAN) and (5) the right frontal eye field (FEF) and inferior parietal sulcus (IPS) (DAN). White matter tracts were generated using probabilistic tractography in a group of ten young healthy volunteers. (b) The bar charts show fractional anisotropy (FA) ± SEM within each tract compared between patients (grey) and age-matched controls (white). *p<0.05, **p<0.005 statistical significance. R: Right, L: Left. Lower FA reflects lower structural integrity. Reproduced from Bonnelle, V, Ham, TE, Leech, R, Kinnunen, KM, Mehta, MA, Greenwood, RJ and Sharp, DJ Salience network integrity predicts default mode network function after traumatic brain injury, Proceedings of the National Academy of Sciences of the United States of America, 109(12), pp. 4690–4695. Copyright © 2012, The National Academy of Sciences, USA.

2010), a region which is frequently damaged after TBI (Kraus et al. 2007; Rutgers et al. 2008; Sharp et al. 2011).

Sustained attention

TBI patients often suffer from sustained attention deficits, which manifest themselves as increased distractibility, poor concentration and a decreased ability to maintain attention focused over a long period of time, suggesting both a decrease in vigilance level and a vigilance decrement over time (Stuss et al. 1989; Whyte et al. 1995; Dockree et al. 2004). These impairments have been found to be closely related to other executive function deficits in these patients, such as performance monitoring (McAvinue et al. 2005; O’Keeffe et al. 2007) and inhibitory control (Robertson et al. 1997).

(p. 1040) A number of studies have used measures of vigilance level such as variability in RTs (Stuss et al. 1994; Whyte et al. 1995) or increased error rates (Robertson et al. 1997) to investigate sustained attention deficits after TBI. However, a possible limitation of these measures is that RT inconsistencies or errors could also reflect difficulties due to the specific cognitive demands of the task and are not necessarily due to sustained attention deficits (Malhotra, Coulthard, and Husain 2009). Impairment of sustained attention might thus be best demonstrated through decline in performance (RT and/or accuracy) over the duration of a task that patients are initially able to perform well. In keeping with that, many studies investigating groups of moderate to severe TBI have observed that patients often perform tasks well initially, but fail to maintain their attention focused toward the end, leading to impaired performance over time (Loken et al. 1995; Whyte et al. 1995; Bonnelle et al. 2011) (Fig. 34.3a). However, this vigilance decrement is not a consistent finding (Parasuraman, Mutter, and Molloy 1991). This might be due to differences in paradigms used; studies using tasks where the response can become automated over time might fail to observe a performance decrement.

Neurological Disorders of AttentionClick to view larger

Figure 34.3 Vigilance decrement deficits after TBI are associated with changes in DMN function and structure. (a) Mean reaction time (RT) during the first (T1) and the last third (T3) of a simple choice reaction time task, plotted for TBI patients in orange and age matched controls in blue. (b) Sagittal view of the brain regions showing increase activation over time in patients with high change in RT compared to patients in low change in RT (red-yellow). The right cingulum bundle connecting the posterior and anterior parts of the DMN is shown in green (from the JHU White Matter Tractography Atlas). (c) Fractional anisotropy (FA) of the cingulum bundle in patients is plotted against the change in RT between the first and the last part of the task (N=28). Measures are age-normalized, i.e. age was regressed out from the measures using a linear regression, where residuals were saved as standardized values. Adapted from Bonnelle, V., Leech, R., Kinnunen, K. M., Ham, T. E., Beckmann, C. F., De Boissezon, X., Greenwood, R. J., and Sharp, D. J., Default mode network connectivity predicts sustained attention deficits after traumatic brain injury, Journal of Neuroscience, 31(38), pp. 13442–13451 © 2011, The Society for Neuroscience.

Previous neuroimaging and lesion studies proposed that a predominantly right-lateralized fronto-parietal system supports sustained attention (Wilkins et al. 1987; Pardo et al. 1991; Rueckert and Grafman 1996; Coull et al. 1998). Nonetheless, no studies to date have described a relationship between sustained attention deficits and white matter integrity in the right SLF after TBI. However, increased activation within (p. 1041) the DMN has been associated with vigilance decrement in TBI patients (Bonnelle et al. 2011) (Fig. 34.3b). The cingulum bundles, which are the major tracts connecting the anterior and posterior parts of the DMN (Greicius et al. 2009) (Fig. 34.3b), have consistently been found to be damaged after TBI (Kraus et al. 2007; Niogi et al. 2008b; Bonnelle et al. 2011, 2012). Decreased structural integrity of the right cingulum bundle has been associated with greater vigilance decrement in TBI patients (Bonnelle et al. 2011) (Fig. 34.3c), and has also been implicated in individual differences in sustained attention in the normal brain (Takahashi et al. 2010).

Divided and executive attention

Most evidence for impaired divided attention in TBI patients comes from studies investigating dual-task performance. In general, patients’ deficits in divided attention tend to manifest under conditions of high cognitive load rather than when tasks are relatively simple and automatic (Park, Moscovitch, and Robertson 1999; Leclercq et al. 2000; Brouwer et al. 2001; Azouvi et al. 2004). These deficits do not appear to be due to impairments of strategic allocation of attention, switching between tasks or working memory. Indeed, a dual-task performance decrement, with preserved ability to allocate attention resources preferentially to one task or the other according to instructions, has been described after TBI (Azouvi et al. 2004). The difficulty in dealing with two tasks at a time might result from limited attention resources, consistent with the high frequency of fatigue complaints after TBI, perhaps resulting from additional mental effort required by patients to manage a complex task and compensate for lower attention resources (Belmont et al. 2006; Belmont, Agar, and Azouvi 2009; Ashman et al. 2008).

Divided attention does not appear to be associated with one specific brain system. The increased attention resource recruitment characteristic of divided attention has often been associated with increased recruitment of contralateral brain regions. For instance, when gradually increasing difficulty level on a visual attention task, easier conditions typically begin with mainly right-sided activity, but as conditions become more difficult, left-lateralized homologue areas activate (Nebel et al. 2005). One prediction would therefore be that the structural integrity of the corpus callosum, consistently found to be damaged after TBI (Kraus et al. 2007; Kumar et al. 2009; Niogi et al. 2008b), might play an important role in supporting the need for an increase in attention resource. However, most TBI studies have failed to relate white matter integrity within the corpus callosum to specific attention measures (Mathias et al. 2004; Little et al. 2010). Furthermore, although divided attention deficits after TBI have been extensively investigated behaviourally, no study to our knowledge has specifically investigated the relationship between divided attention and white matter damage in TBI patients.

Deficits in many aspects of executive functions, such as conflict and error monitoring or inhibitory control, have been reported in TBI (Hart et al. 1998; O’Keeffe, Dockree, and Robertson 2004; Larson et al. 2007; Dimoska-Di Marco et al. 2011). However, it is not always clear whether executive functions are directly impaired, or whether these deficits are the consequence of other attention impairments. Consider the case (p. 1042) of inhibitory control, which has been extensively studied in healthy volunteers using the stop-signal task (Logan et al. 1984). Several studies found evidence for inhibitory impairment in TBI patients, as reflected by longer stop-signal reaction-time measures (for a review, see Dimoska-Di Marco et al. 2011). However, this behavioural measure has been found to be influenced by other factors including focused and sustained attention, or motivation (Boehler et al. 2010; Leotti and Wager 2010).

Interestingly, comparison of the pattern of brain activation during the stop-signal task in TBI and control subjects revealed no difference in frontal regions considered to be involved in inhibitory control or attention capture, but major differences were observed in the DMN (Bonnelle et al. 2012). This was the result of patients showing less DMN deactivation on stop trials (Fig. 34.4b). This decrease in DMN deactivation was associated with poorer inhibitory performance, suggesting that, similarly to what has been observed in ADHD (Liddle et al. 2011), a deficit in attention regulation, or more generally a failure of task engagement, rather than a deficit in motor response inhibition, underlies inhibitory control deficit in TBI patients.

Neurological Disorders of AttentionClick to view larger

Figure 34.4 White matter damage between the right anterior insula and the ACC/pre-SMA relates to DMN deactivation and inhibitory performance during the stop-signal task. (a) Coronal view of the white matter connection between rAI and (blue) overlaid on the activation map for the contrast comparing successful inhibition to go trials in patients (orange), superimposed on the MNI 152 T1 1mm brain template. (b) Sagittal view of the brain regions showing negatively correlated activation with FA measures within the rAI-pre-SMA/dACC tract for StC relative to Go trials, superimposed on the MNI 152 T1 2mm brain template. Cluster corrected Z=2.3, p <0.05. (c) Comparison of stop signal reaction times (SSRT) between TBI patients with low (white) and high (grey) fractional anisotropy (FA) within the raI- pre-SMA/dACC tract.

It has recently been proposed that the Salience network, and more particularly the right anterior insula, might play an important role in rapidly and dynamically ‘switching’ between other large-scale networks (including the DMN) to facilitate rapid access to attention resources when a salient stimulus is detected (Sridharan, Levitin, and Menon 2008; Menon and Uddin 2010). In keeping with this proposal, white matter connections between regions of this network have been found to be particularly damaged after TBI (Fig. 34.2, panels a and b (1), rAI-dACC/preSMA connection), and importantly this has been associated with changes in DMN function as well as impaired inhibitory performance (Bonnelle et al. 2012) (Fig. 34.4b and c).

(p. 1043) Executive dysfunctions after TBI are also likely to be the result of structural disconnections between ACC and other frontal and parietal brain regions. For example, the corona radiata, which connects ACC to DLPFC, is one of the most frequently damaged white matter tracts after TBI (Niogi and Mukherjee 2010). The structural integrity of this tract has often been related to executive functions, especially conflict monitoring, both in healthy individuals (Niogi et al. 2010) and in TBI patients (Kraus et al. 2007; Niogi et al. 2008a). The cingulum bundle constitutes another important pathway via which the ACC connects with posterior regions of the brain. Damage within the cingulum bundles has been associated with lower cognitive performance on the Eriksen flanker task, measuring interference and conflict processing (Wilde et al. 2010). Performance on the Stroop test has also been used as a measure of executive attention in TBI, but results have not always been consistent (Ponsford and Kinsella 1992; Chan 2002). A potential limitation, just as for other measures, is that performance might reflect deficits in speed of information processing as well as several executive functions (Spikman, van Zomeren, and Deelman 1996; Rios, Perianez, and Munoz-Cespedes 2004; Ben-David, Nguyen, and van Lieshout 2011).

To conclude, it is difficult to generalize on the impact of TBI on attention at the group level because of the heterogeneity of this clinical population (each patient presents with a unique combination of focal and diffuse injury). Recent studies suggest that the disruption of specific white matter tracts might explain the occurrence of specific types of attentional deficits. However, further research is required to clarify the impact of axonal injury on the different aspects of attention.

Attention Deficits in Parkinson’s Disease

The previous sections have dealt with the effects of focal lesions on the cortical nodes of attention networks and the effects of TBI, focusing largely on its effects on white matter connections of attention networks. By the very nature of the underlying pathologies, most of those studies have investigated patients with abrupt, acquired brain damage. Parkinson’s disease (PD), by contrast, is a slowly progressive neurodegenerative disorder, traditionally characterized by difficulty in initiating movements, motor slowing, stiffness, and sometimes tremor. It is associated with destruction of dopaminergic neurons in the substantia nigra pars compacta with a pathological signature of accumulation of cytoplasmic protein aggregates known as Lewy bodies. In addition, there is substantial cholinergic denervation which is evident even in early PD (Bohnen and Albin 2011).

Over the last three decades, it has become clear that PD is not just a motor disorder. Many patients develop cognitive deficits, including deficits of attention. Indeed, recent imaging studies have revealed that cortical hypometabolism in PD patients with (p. 1044) cognitive impairment involves regions within medial and lateral parietal and frontal cortex (Huang et al. 2007) that have been identified as critical nodes in attention networks (cf. Fig. 34.5a and Fig. 34.1). Furthermore, cognitive impairment is associated with white matter changes as indexed by diffusion tensor imaging in a large series of patients (Hattori et al. 2012) (Fig. 34.5b, c, and d). Thus PD is associated with slow degeneration of both attentional networks’ nodes and white matter connections, and thus serves as an important model of neurodegeneration affecting these networks to compromise function. In addition, neurochemical alterations in the degenerating PD brain, particularly dopaminergic and cholinergic depletion, provide important insights into neuromodulation of attention in normal brains.

In PD with established motor symptoms and signs, cognitive impairments may eventually lead to dementia or Parkinson’s disease with dementia (PDD). Alternatively, dementia may be evident before or occur simultaneously with motor deficits, leading to a diagnosis of dementia with Lewy bodies (DLB). PDD and DLB are probably different manifestations of the same underlying pathological process (McKeith and Mosimann 2004). Both cortical and white matter changes increase in PDD patients compared to those with PD without dementia (e.g. see Fig. 34.5c). For example, among other tracts, the superior longitudinal fasciculus has been found to be disrupted in PDD patients, correlating with patients’ cognitive performance (Hattori et al. 2012) (Fig. 34.5d).

Selective attention

Although there have been several studies of selective attention in PD, the findings have been variable. On the one hand, deficits in covert exogenous orienting of attention have been reported in some patients (see Nys, Santens, and Vingerhoets 2010). However, several studies have demonstrated excessive exogenous orienting compared to healthy controls. For example, Briand et al. (2001) showed that exogenous non-predictive cues in a Posner task give a bigger initial facilitation in PD. Similarly, Wright et al. (1990) reported that although Parkinson’s patients are slower overall, they incur smaller costs for invalid pre-cues (at 1100 ms). The interpretation of these investigators was that PD patients disengage from attended locations more readily. Using the Attentional Network Task protocol, Zhou et al. (2012) also documented that patients have stronger exogenous orienting than controls, but no difference in flanker conflict or alerting.

Neurological Disorders of AttentionClick to view larger

Figure 34.5 Changes in brain metabolism and white matter structure in Parkinson’s disease. (a) Parkinson's disease-related cognitive pattern identified by spatial covariance analysis of FDG PET scans is characterized by co-varying bilateral metabolic reductions in the rostral supplementary motor area (pre-SMA) and precuneus, the dorsal premotor cortex, and the inferior parietal lobule, as well as in the left prefrontal region (blue) and metabolic increases in the cerebellar vermis and dentate nuclei. Adapted from Huang et al. 2007. Comparison of white matter structure between PD patients with dementia (PDD) and controls (b), and PDD patients and PD patients with no evidence of cognitive deficits (c). White matter tracts highlighted in yellow have significantly reduced fractional anisotropy in PDD. (d) Fractional anisotropy within the white matter sections highlighted in yellow are significantly correlated with Mini-Mental State Examination (MMSE) scores. Adapted from Takaaki Hattori, Satoshi Orimo, Shigeki Aoki, Kenji Ito, Osamu Abe, Atsushi Amano, Ryo Sato, Kasumi Sakai, and Hidehiro Mizusawa, Cognitive status correlates with white matter alteration in Parkinson’s disease, Human Brain Mapping, 33(3), pp. 727–39 © 2011, Wiley Periodicals, Inc., with permission from John Wiley & Sons.

On visual search, Troscianko and Calvert (1993) first demonstrated impairments in pop-out search but no impairment on serial or conjunction search in PD (but see Berry et al. 1999). Similarly, Filoteo et al. (1997) showed deficits on a simple visual search task, but less slowing than controls on the conjunction task. The conclusion from a series of visual search tasks that altered attention demands was that PD patients were most impaired on so-called ‘preattentive tasks’, requiring significantly greater orientation differences between target and distractors or longer stimulus durations to find stimuli (Lieb et al. 1999), perhaps reflecting impaired low-level saliency processing (Mannan et al. 2008). DLB patients, who by definition have more advanced cognitive impairment, have been (p. 1045) (p. 1046) reported to be bad at both pop-out and serial search, compared to PD patients without dementia and individuals with Alzheimer’s disease (Cormack et al. 2004). Thus deficits in more demanding conjunction search are evident with greater cognitive deficits in PD.

Indeed, Horowitz et al. (2006) found deficits in both parallel and conjunction search in PD patients without dementia when the search target was unknown, as well as when stimulus-driven information decreased in salience. They interpreted this as evidence for deficits at a higher level than salience processing. Evidence that attention deficits in PD may also occur at later stages comes from findings that patients fail to learn from contextual cues in visual search (van Asselen et al. 2009).

There has also been much controversy about inhibition of previously selected attended locations in PD (Kingstone et al. 2002; Yamaguchi and Kobayashi 1998). One group has claimed that although the benefit of exogenous, non-predictive cues is negatively correlated with disease severity, inhibition of return is positively correlated (Briand et al. 2001). Poliakoff et al. (2003) have reviewed the data on inhibition of return in PD, considering reasons for the contradictory results. They concluded that tactile inhibition of return is reduced in PD patients, and suggest that this might be due to general impairments of inhibitory control, rather than specific attention processes.

Sustained attention

An important clinical feature that has long been recognized in DLB and PDD is fluctuations of attention over even short periods of time. When tested using experimental batteries that include simple or choice reaction time and digit vigilance (rapid serial visual presentation), patients with PDD/DLB have large fluctuations in choice RT and poor vigilance (Ballard et al. 2002). Slow responses in a vigilance task have been correlated with bilateral prefrontal atrophy (Brück et al. 2004). More recently, it has been reported that vigilance is impaired especially in those PD patients who suffer visual hallucinations (Koerts et al. 2010). Although Zhou et al. (2012) found no effect of warning signals on the ANT protocol, other investigators have reported that warning signals have a more transient (though equally strong) alerting effect than in controls on a simple reaction time task (Bloxham, Dick, and Moore 1987). Gait problems and falling in PD are strongly correlated with attention, as measured by sustained reaction speed and RT variability (Allcock et al. 2009). In one study, measures of sustained attention accounted for 10% of the variance in gait speed in PD patients (Lord et al. 2010).

Divided and executive attention

Several studies have reported deficits of divided attention or on dual tasks in PD. For example, PD patients are impaired compared to healthy controls on detection of targets if two auditory streams have to be attended simultaneously (Sharpe 1996). Moreover, using the dual task methodology employed by Baddeley and his colleagues to investigate the ‘central executive’, some authors have demonstrated that PD patients have a (p. 1047) significant decline in performance on a visuomotor tracking task while recalling digit span forward sequences, whereas controls showed no such change (Dalrymple-Alford et al. 1994). Such dual-task deficits can have significant effects on function in everyday life for PD patients, with concurrent tasks significantly slowing gait speed and reducing mean step length (Rochester et al. 2004; Lord et al. 2010; but see Smulders et al. 2012).

Although early studies were inconclusive (Weingartner et al. 1984; Taylor, Saint-Cyr, and Lang 1987), more recent research has revealed that some the greatest impairments of PD patients on a cognitive battery include performance on the trail-making test, consistent with a problem in dividing and/or switching attention (Elgh et al. 2009). The same study also demonstrated that another key performance indicator is the Wisconsin Card-Sorting Test. It has long been known that PD patients are impaired on this task (Bowen et al. 1975; Lees and Smith 1983) with particular difficulty in switching their sorting strategy (Cools et al. 1984). Specifically, extradimensional shifts, i.e. switching classification to be a previously irrelevant stimulus dimension, appear to be particularly difficult (Downes et al. 1989).

Price and colleagues (2009) have recently extensively reviewed studies of categorization tasks such as the Wisconsin Card-Sorting Test, and by fractionating the task components, concluded that under-medicated patients have difficulty in rule shifting (particularly to a previously irrelevant dimension) but are less distractible when required to continue the current rule. In their view, when over-medicated, patients are improved at set shifting, but have difficulty generating rules and cannot effectively use negative feedback to select new rules. Interestingly, deep brain stimulation (DBS) to the subthalamic nucleus (STN)—a treatment option for some patients with PD—also improves performance on the Wisconsin Card-Sorting Test (Jahanshahi et al. 2000).

Some attention deficits in PD may be explainable as a failure of ‘braking’ or inhibition. PD increases errors on go/no-go tasks in a complexity-dependent manner (Cooper et al. 1994). A recent study has revealed that early PD patients who perform within normal limits on a go/no-go task nevertheless have increased prefrontal and basal ganglia activation compared to healthy people, suggesting greater involvement of these structures is necessary to exert similar levels of control (Baglio et al. 2011). On the stop-signal task, PD patients have increased stop-signal reaction times, independent of their ‘go’ reaction times (Gauggel, Rieger, and Feghoff 2004; Obeso et al. 2011). Deep brain stimulation to the subthalamic nucleus (STN) can also increase stop-signal reaction times on the stop-signal task (Ray et al. 2009), although the opposite effect has also been observed (Mirabella et al. 2012). Deep brain stimulation may give varying effects on inhibition depending on the exact site being stimulated, with ventral but not dorsal STN stimulation leading to worse inhibitory control (Hershey et al. 2010). This intervention has also been shown to increase the speed of responses on a go/no-go task, while increasing commission errors (Ballanger et al. 2009).

On the Stroop test, PD patients are worse when task type (ink-naming vs. word-reading) has to be remembered through a block, but not when it changes from trial to trial (Brown and Marsden 1988). Thus, one potential cause for impaired performance on the Stroop might be a deficit in maintaining task set, particularly when no exogenous cues are available. However, Obeso et al. (2011) interpret such deficits in the context of other tests of inhibitory control, including prolonged SSRT and go/no-go task (p. 1048) impairments, pointing to a general disorder of inhibition underlying several aspects of executive attention impairment in PD.


Deficits in directing attention are a major cause of everyday problems and functional impairment across many brain disorders. Here we have focused on patients with focal brain lesions, traumatic brain injury, and Parkinson’s disease to bring out common themes and principles that cut across different underlying pathological processes. We have tried to frame the findings in terms of deficits in selective, sustained, divided, and executive attention. In many ways, of course, this may be an artifice, but such a conceptual framework nevertheless provides a useful means to organize the extensive empirical data that have now emerged from studies of neurological patients. The challenge will be to use this information to develop effective treatments, an area of research that is currently in its infancy.


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