Timothy E. Ham

Salience network integrity predicts default mode network function after traumatic brain injury

Efficient behavior involves the coordinated activity of large-scale brain networks, but the way in which these networks interact is uncertain. One theory is that the salience network (SN)—which includes the anterior cingulate cortex, presupplementary motor area, and anterior insulae—regulates dynamic changes in other networks. If this is the case, then damage to the structural connectivity of the SN should disrupt the regulation of associated networks. To investigate this hypothesis, we studied a group of 57 patients with cognitive impairments following traumatic brain injury (TBI) and 25 control subjects using the stop-signal task. The pattern of brain activity associated with stop-signal task performance was studied by using functional MRI, and the structural integrity of network connections was quantified by using diffusion tensor imaging. Efficient inhibitory control was associated with rapid deactivation within parts of the default mode network (DMN), including the precuneus and posterior cingulate cortex. TBI patients showed a failure of DMN deactivation, which was associated with an impairment of inhibitory control. TBI frequently results in traumatic axonal injury, which can disconnect brain networks by damaging white matter tracts. The abnormality of DMN function was specifically predicted by the amount of white matter damage in the SN tract connecting the right anterior insulae to the presupplementary motor area and dorsal anterior cingulate cortex. The results provide evidence that structural integrity of the SN is necessary for the efficient regulation of activity in the DMN, and that a failure of this regulation leads to inefficient cognitive control.

Default Mode Network Connectivity Predicts Sustained Attention Deficits after Traumatic Brain Injury

Traumatic brain injury (TBI) frequently produces impairments of attention in humans. These can result in a failure to maintain consistent goal-directed behavior. A predominantly right-lateralized frontoparietal network is often engaged during attentionally demanding tasks. However, lapses of attention have also been associated with increases in activation within the default mode network (DMN). Here, we study TBI patients with sustained attention impairment, defined on the basis of the consistency of their behavioral performance over time. We show that sustained attention impairments in patients are associated with an increase in DMN activation, particularly within the precuneus and posterior cingulate cortex. Furthermore, the interaction of the precuneus with the rest of the DMN at the start of the task, i.e., its functional connectivity, predicts which patients go on to show impairments of attention. Importantly, this predictive information is present before any behavioral evidence of sustained attention impairment, and the relationship is also found in a subgroup of patients without focal brain damage. TBI often results in diffuse axonal injury, which produces cognitive impairment by disconnecting nodes in distributed brain networks. Using diffusion tensor imaging, we demonstrate that structural disconnection within the DMN also correlates with the level of sustained attention. These results show that abnormalities in DMN function are a sensitive marker of impairments of attention and suggest that changes in connectivity within the DMN are central to the development of attentional impairment after TBI.

Cognitive Control and the Salience Network: An Investigation of Error Processing and Effective Connectivity

The Salience Network (SN) consists of the dorsal anterior cingulate cortex (dACC) and bilateral insulae. The network responds to behaviorally salient events, and an important question is how its nodes interact. One theory is that the dACC provides the earliest cortical signal of behaviorally salient events, such as errors. Alternatively, the anterior right insula (aRI) has been proposed to provide an early cognitive control signal. As these regions frequently coactivate, it has been difficult to disentangle their roles using conventional methods. Here we use dynamic causal modeling and a Bayesian model evidence technique to investigate the causal relationships between nodes in the SN after errors. Thirty-five human subjects performed the Simon task. The task has two conditions (congruent and incongruent) producing two distinct error types. Neural activity associated with errors was investigated using fMRI. Subjects made a total of 1319 congruent and 1617 incongruent errors. Errors resulted in robust activation of the SN. Dynamic causal modeling analyses demonstrated that input into the SN was most likely via the aRI for both error types and that the aRI was the only region intrinsically connected to both other nodes. Only incongruent errors produced behavioral adaptation, and the strength of the connection between the dACC and the left insulae correlated with the extent of this behavioral change. We conclude that the aRI, not the dACC, drives the SN after errors on an attentionally demanding task, and that a change in the effective connectivity of the dACC is associated with behavioral adaptation after errors.

Investigating white matter injury after mild traumatic brain injury.

PURPOSE OF REVIEW Traumatic brain injury (TBI) often results in traumatic axonal injury (TAI). This is difficult to identify using conventional neuroimaging methods. We review recent work that uses advanced imaging methods to identify TAI following mild (m)TBI. RECENT FINDINGS Susceptibility-weighted imaging (SWI) is a highly sensitive way of identifying microbleeds, which are a marker of TAI. Diffusion tensor imaging (DTI) provides a more flexible way of investigating white matter injury. Recent studies largely confirm that DTI is sensitive to white matter damage after mTBI. Distinct DTI abnormalities are observed in the acute and subacute/chronic stages. DTI measurements change dynamically after an injury, reflecting the evolving pathological processes. DTI abnormalities correlate with cognitive and neuropsychiatric impairments. Importantly, DTI can contribute to the prediction of clinical outcome and has begun to be applied to the study of sports and blast injury. SUMMARY DTI and SWI are important advances in MRI that allow more detailed investigation of white matter injury. SWI is a highly sensitive way of identifying microbleeds. DTI is a flexible way of quantifying white matter integrity, and provides a method of diagnosing clinically significant white matter injury when conventional imaging is normal.

Damage to the Salience Network and Interactions with the Default Mode Network

Interactions between the Salience Network (SN) and the Default Mode Network (DMN) are thought to be important for cognitive control. However, evidence for a causal relationship between the networks is limited. Previously, we have reported that traumatic damage to white matter tracts within the SN predicts abnormal DMN function. Here we investigate the effect of this damage on network interactions that accompany changing motor control. We initially used fMRI of the Stop Signal Task to study response inhibition in humans. In healthy subjects, functional connectivity (FC) between the right anterior insula (rAI), a key node of the SN, and the DMN transiently increased during stopping. This change in FC was not seen in a group of traumatic brain injury (TBI) patients with impaired cognitive control. Furthermore, the amount of SN tract damage negatively correlated with FC between the networks. We confirmed these findings in a second group of TBI patients. Here, switching rather than inhibiting a motor response: (1) was accompanied by a similar increase in network FC in healthy controls; (2) was not seen in TBI patients; and (3) tract damage after TBI again correlated with FC breakdown. This shows that coupling between the rAI and DMN increases with cognitive control and that damage within the SN impairs this dynamic network interaction. This work provides compelling evidence for a model of cognitive control where the SN is involved in the attentional capture of salient external stimuli and signals the DMN to reduce its activity when attention is externally focused.

Individual prediction of white matter injury following traumatic brain injury

Traumatic brain injury (TBI) often results in traumatic axonal injury (TAI). This can be difficult to identify using conventional imaging. Diffusion tensor imaging (DTI) offers a method of assessing axonal damage in vivo, but has previously mainly been used to investigate groups of patients. Machine learning techniques are increasingly used to improve diagnosis based on complex imaging measures. We investigated whether machine learning applied to DTI data can be used to diagnose white matter damage after TBI and to predict neuropsychological outcome in individual patients.

Pituitary dysfunction after blast traumatic brain injury: The UK BIOSAP study.

Pituitary dysfunction is a recognized consequence of traumatic brain injury (TBI) that causes cognitive, psychological, and metabolic impairment. Hormone replacement offers a therapeutic opportunity. Blast TBI (bTBI) from improvised explosive devices is commonly seen in soldiers returning from recent conflicts. We investigated: (1) the prevalence and consequences of pituitary dysfunction following moderate to severe bTBI and (2) whether it is associated with particular patterns of brain injury.

How can investigation of network function inform rehabilitation after traumatic brain injury

PURPOSE OF REVIEW Traumatic brain injury (TBI) often results in long-term cognitive impairments. This is often due to the disruption of brain networks that support cognition. Major advances have recently been made in our understanding of these networks. Here we review work that investigates the effect of TBI on brain networks, and discuss the potential importance of these findings for rehabilitation. RECENT FINDINGS Large-scale brain networks, which we refer to as intrinsic connectivity networks (ICNs), have been identified. Traumatic axonal injury disrupts their white-matter connections, and altered brain activity within the networks is frequently observed after TBI. These changes relate to the pattern of cognitive impairment, and are useful for predicting clinical outcome. The effect of drugs such as methylphenidate, which can be used to augment rehabilitation, are beginning to be studied in the context of their effect on network function after TBI. SUMMARY The assessment of brain network function after TBI provides insights into the pathophysiology of cognitive dysfunction and the mechanisms involved in recovery. These advances should provide the basis for a more detailed understanding of rehabilitation, and ultimately guide the development of targeted individualized therapy after TBI.

A robust method for investigating thalamic white matter tracts after traumatic brain injury.

Damage to the structural connections of the thalamus is a frequent feature of traumatic brain injury (TBI) and can be a key factor in determining clinical outcome. Until recently it has been difficult to quantify the extent of this damage in vivo. Diffusion tensor imaging (DTI) provides a validated method to investigate traumatic axonal injury, and can be applied to quantify damage to thalamic connections. DTI can also be used to assess white matter tract structure using tractography, and this technique has been used to study thalamo-cortical connections in the healthy brain. However, the presence of white matter injury can cause failure of tractography algorithms. Here, we report a method for investigating thalamo-cortical connectivity that bypasses the need for individual tractography. We first created a template for a number of thalamo-cortical connections using probabilistic tractography performed in ten healthy subjects. This template for investigating white matter structure was validated by comparison with individual tractography in the same group, as well as in an independent control group (N = 11). We also evaluated two methods of masking tract location using the tract skeleton generated by tract based spatial statistics, and a cerebrospinal fluid mask. Voxel-wise estimates of fractional anisotropy derived from the template were more strongly correlated with individual tractography when both types of masking were used. The tract templates were then used to sample DTI measures from a group of TBI patients (N = 22), with direct comparison performed against probabilistic tractography in individual patients. Probabilistic tractography often failed to produce anatomically plausible tracts in TBI patients. Importantly, we show that this problem increases as tracts become more damaged, and leads to underestimation of the amount of traumatic axonal injury. In contrast, the tract template can be used in these cases, allowing a more accurate assessment of white matter damage. In summary, we propose a method suitable for assessing specific thalamo-cortical white matter connections after TBI that is robust to the presence of varying amounts of traumatic axonal injury, as well as highlighting the potential problems of applying tractography algorithms in patient populations.

Predicting beneficial effects of atomoxetine and citalopram on response inhibition in Parkinson's disease with clinical and neuroimaging measures

Recent studies indicate that selective noradrenergic (atomoxetine) and serotonergic (citalopram) reuptake inhibitors may improve response inhibition in selected patients with Parkinsons disease, restoring behavioral performance and brain activity. We reassessed the behavioral efficacy of these drugs in a larger cohort and developed predictive models to identify patient responders. We used a double‐blind randomized three‐way crossover design to investigate stopping efficiency in 34 patients with idiopathic Parkinsons disease after 40 mg atomoxetine, 30 mg citalopram, or placebo. Diffusion‐weighted and functional imaging measured microstructural properties and regional brain activations, respectively. We confirmed that Parkinsons disease impairs response inhibition. Overall, drug effects on response inhibition varied substantially across patients at both behavioral and brain activity levels. We therefore built binary classifiers with leave‐one‐out cross‐validation (LOOCV) to predict patients’ responses in terms of improved stopping efficiency. We identified two optimal models: (1) a “clinical” model that predicted the response of an individual patient with 77–79% accuracy for atomoxetine and citalopram, using clinically available information including age, cognitive status, and levodopa equivalent dose, and a simple diffusion‐weighted imaging scan; and (2) a “mechanistic” model that explained the behavioral response with 85% accuracy for each drug, using drug‐induced changes of brain activations in the striatum and presupplementary motor area from functional imaging. These data support growing evidence for the role of noradrenaline and serotonin in inhibitory control. Although noradrenergic and serotonergic drugs have highly variable effects in patients with Parkinsons disease, the individual patients response to each drug can be predicted using a pattern of clinical and neuroimaging features. Hum Brain Mapp 37:1026–1037, 2016.