Predicting functional impairments with lesion-derived disconnectome mapping: Validation in stroke patients with motor deficits.

Focal structural damage to white matter tracts can result in functional deficits in stroke patients. Traditional voxel-based lesion-symptom mapping is commonly used to localize brain structures linked to neurological deficits. Emerging evidence suggests that the impact of structural focal damage may extend beyond immediate lesion sites. In this study, we present a disconnectome mapping approach based on support vector regression (SVR) to identify brain structures and white matter pathways associated with functional deficits in stroke patients. For clinical validation, we utilized imaging data from 340 stroke patients exhibiting motor deficits. A disconnectome map was initially derived from lesions for each patient. Bootstrap sampling was then employed to balance the sample size between a minority group of patients exhibiting right or left motor deficits and those without deficits. Subsequently, SVR analysis was used to identify voxels associated with motor deficits (p < .005). Our disconnectome-based analysis significantly outperformed alternative lesion-symptom approaches in identifying major white matter pathways within the corticospinal tracts associated with upper-lower limb motor deficits. Bootstrapping significantly increased the sensitivity (80%-87%) for identifying patients with motor deficits, with a minimum lesion size of 32 and 235 mm3 for the right and left motor deficit, respectively. Overall, the lesion-based methods achieved lower sensitivities compared with those based on disconnection maps. The primary contribution of our approach lies in introducing a bootstrapped disconnectome-based mapping approach to identify lesion-derived white matter disconnections associated with functional deficits, particularly efficient in handling imbalanced data.

Assessing the effects of head modelling errors and measurement noise on EEG source localization accuracy in preterm newborns: A single-subject study.

The accuracy of electroencephalogram (EEG) source localization is compromised because of head modelling errors. In this study, we investigated the effect of inaccuracy in the conductivity of head tissues and head model structural deficiencies on the accuracy of EEG source analysis in premature neonates. A series of EEG forward and inverse simulations was performed by introducing structural deficiencies into the reference head models to generate test models, which were then used to investigate head modelling errors caused by cerebrospinal fluid (CSF) exclusion, lack of grey matter (GM)-white matter (WM) distinction, fontanel exclusion and inaccuracy in skull conductivity. The modelling errors were computed between forward and inverse solutions obtained using the reference and test models generated for each deficiency. Our results showed that the exclusion of CSF from the head model had a strong widespread effect on the accuracy of the EEG source localization with position errors lower than 4.17 mm. The GM and WM distinction also caused strong localization errors (up to 3.5 mm). The exclusion of fontanels from the head model also strongly affected the accuracy of the EEG source localization for sources located beneath the fontanels with a maximum localization error of 4.37 mm. Similarly, inaccuracies in the skull conductivity caused errors in EEG forward and inverse modelling in sources beneath cranial bones. Our results indicate that the accuracy of EEG source imaging in premature neonates can be largely improved by using head models, which include not only the brain, skull and scalp but also the CSF, GM, WM and fontanels.

Determinants of disability at 6 months after stroke: The GRECogVASC Study.

BACKGROUND AND PURPOSE: The aim of this study was to determine the contributions of background disorders responsible for participation restriction as indexed by a structured interview for the modified Rankin Scale (mRS-SI). METHODS: A subset of 256 patients was assessed at 6 months after stroke using the National Institutes of Health Stroke Scale (NIHSS), gait score, comprehensive cognitive battery (yielding a global cognitive Z-score), behavioral dysexecutive disorders (DDs), anxiety and depressive symptoms, epilepsy, and headache. Following bivariate analyses, determinants of participation restriction were selected using ordinal regression analysis with partial odds. RESULTS: Poststroke participation restriction (mRS-SI score > 1) was observed in 59% of the patients. In bivariate analyses, mRS-SI score was associated with prestroke mRS-SI score, 6-month NIHSS score, gait score, global cognitive Z-score, behavioral DDs, and presence of anxiety and depression (all: p = 0.0001; epilepsy: p =0.3; headache: p = 0.7). After logistic regression analysis, NIHSS score was associated with increasing mRS-SI score (p = 0.00001). Prestroke mRS-SI score (p = 0.00001), behavioral DDs (p = 0.0008) and global cognitive Z-score (p = 0.01) were associated with both mRS-SI score > 1 and mRS-SI score > 2. In addition, gait score was associated with mRS-SI score > 2 (p = 0.00001). This model classified 85% of mRS-SI scores correctly (p = 0.001). Structural equation modeling showed the contributions of gait limitation (standardized coefficient [SC]: 0.68; p = 0.01), prestroke mRS-SI (SC: 0.41; p = 0.01), severity of neurological impairment (SC: 0.16; p = 0.01), global cognitive Z-score (SC: -0.14; p = 0.05), and behavioral DDs (SC: 0.13; p = 0.01). CONCLUSION: These results provide a statistical model of weights of determinants responsible for poststroke participation restriction and highlight a new independent determinant: behavioral DDs.

Effect of confounding variables on hemodynamic response function estimation using averaging and deconvolution analysis: An event-related NIRS study.

Slow and rapid event-related designs are used in fMRI and functional near-infrared spectroscopy (fNIRS) experiments to temporally characterize the brain hemodynamic response to discrete events. Conventional averaging (CA) and the deconvolution method (DM) are the two techniques commonly used to estimate the Hemodynamic Response Function (HRF) profile in event-related designs. In this study, we conducted a series of simulations using synthetic and real NIRS data to examine the effect of the main confounding factors, including event sequence timing parameters, different types of noise, signal-to-noise ratio (SNR), temporal autocorrelation and temporal filtering on the performance of these techniques in slow and rapid event-related designs. We also compared systematic errors in the estimates of the fitted HRF amplitude, latency and duration for both techniques. We further compared the performance of deconvolution methods based on Finite Impulse Response (FIR) basis functions and gamma basis sets. Our results demonstrate that DM was much less sensitive to confounding factors than CA. Event timing was the main parameter largely affecting the accuracy of CA. In slow event-related designs, deconvolution methods provided similar results to those obtained by CA. In rapid event-related designs, our results showed that DM outperformed CA for all SNR, especially above -5 dB regardless of the event sequence timing and the dynamics of background NIRS activity. Our results also show that periodic low-frequency systemic hemodynamic fluctuations as well as phase-locked noise can markedly obscure hemodynamic evoked responses. Temporal autocorrelation also affected the performance of both techniques by inducing distortions in the time profile of the estimated hemodynamic response with inflated t-statistics, especially at low SNRs. We also found that high-pass temporal filtering could substantially affect the performance of both techniques by removing the low-frequency components of HRF profiles. Our results emphasize the importance of characterization of event timing, background noise and SNR when estimating HRF profiles using CA and DM in event-related designs.

Hemodynamic Changes Associated with Interictal Spikes Induced by Acute Models of Focal Epilepsy in Rats: A Simultaneous Electrocorticography and Near-Infrared Spectroscopy Study.

Interictal spikes can be generated by blocking GABAA receptor-mediated inhibition. The nature of the hemodynamic activities associated with interictal spikes in acute models of focal epilepsy based on GABA deactivation has not been determined. We analyzed systemic changes in hemodynamic signals associated with interictal spikes generated by acute models of focal epilepsy. Simultaneous ElectroCorticoGraphy (ECoG) and Near-InfraRed Spectroscopy (NIRS) recordings were obtained in vivo from adult Sprague-Dawley rat brain during semi-periodic focal interictal spikes induced by local cortical application of low doses of Penicillin G (PG) and Bicuculline Methiodide (BM) as GABA deactivation agents. The Finite Impulse Response deconvolution technique was used to estimate the profile of hemodynamic changes in oxyhemoglobin (HbO) and deoxyhemoglobin (HbR) concentrations associated with interictal ECoG spikes in each rat. Our results show that, in both acute models of focal epilepsy, the hemodynamic changes associated with interictal spikes were characterized by pre-spike and post-spike primary NIRS responses, and recovery periods with slight differences in amplitude and latency. The pre-spike period starting at least 2 s prior to the onset of ECoG spikes was characterized by a significant decrease in HbO concomitant with an increase in HbR with respect to baseline. The post-spike primary NIRS response exhibited the expected changes described according to the classical view of neurovascular coupling, i.e., a significant increase in HbO and a significant decrease in HbR in response to interictal spikes. The recovery period was characterized by a decreased HbO signal and an increased HbR signal, followed by a return to baseline. Compared to the BM epilepsy model, the PG model was more stable and showed lower variability in the shape, amplitude and latency of the components of spike-related hemodynamic changes. Our findings support a prominent role for pre-spike hemodynamic changes in the initiation of interictal spikes. The mechanism of interactions between neuronal and vascular networks during the pre-spike period constitutes a complex process, resulting in increased sensitivity of the epileptogenic focus to induce neuronal spiking.

Effects of uncertainty in head tissue conductivity and complexity on EEG forward modeling in neonates.

In this study, we investigated the impact of uncertainty in head tissue conductivities and inherent geometrical complexities including fontanels in neonates. Based on MR and CT coregistered images, we created a realistic neonatal head model consisting of scalp, skull, fontanels, cerebrospinal fluid (CSF), gray matter (GM), and white matter (WM). Using computer simulations, we investigated the effects of exclusion of CSF and fontanels, discrimination between GM and WM, and uncertainty in conductivity of neonatal head tissues on EEG forward modeling. We found that exclusion of CSF from the head model induced the strongest widespread effect on the EEG forward solution. Discrimination between GM and white matter also induced a strong widespread effect, but which was less intense than that of CSF exclusion. The results also showed that exclusion of the fontanels from the neonatal head model locally affected areas beneath the fontanels, but this effect was much less pronounced than those of exclusion of CSF and GM/WM discrimination. Changes in GM/WM conductivities by 25% with respect to reference values induced considerable effects in EEG forward solution, but this effect was more pronounced for GM conductivity. Similarly, changes in skull conductivity induced effects in the EEG forward modeling in areas covered by the cranial bones. The least intense effect on EEG was caused by changes in conductivity of the fontanels. Our findings clearly emphasize the impact of uncertainty in conductivity and deficiencies in head tissue compartments on modeling research and localization of brain electrical activity in neonates. Hum Brain Mapp 37:3604-3622, 2016. © 2016 Wiley Periodicals, Inc.

Enhanced ADHD classification through deep learning and dynamic resting state fMRI analysis.

Attention Deficit Hyperactivity Disorder (ADHD) is characterized by deficits in attention, hyperactivity, and/or impulsivity. Resting-state functional connectivity analysis has emerged as a promising approach for ADHD classification using resting-state functional magnetic resonance imaging (rs-fMRI), although with limited accuracy. Recent studies have highlighted dynamic changes in functional connectivity patterns among ADHD children. In this study, we introduce Skip-Vote-Net, a novel deep learning-based network designed for classifying ADHD from typically developing children (TDC) by leveraging dynamic connectivity analysis on rs-fMRI data collected from 222 participants included in the NYU dataset within the ADHD-200 database. Initially, for each subject, functional connectivity matrices were constructed from overlapping segments using Pearson's correlation between mean time series of 116 regions of interest defined by the Automated Anatomical Labeling (AAL) 116 atlas. Skip-Vote-Net was then developed, employing a majority voting mechanism to classify ADHD/TDC children, as well as distinguishing between the two main subtypes: the inattentive subtype (ADHDI) and the predominantly combined subtype (ADHDC). The proposed method was evaluated across four classification scenarios: (1) two-class classification of ADHD from TD children using balanced data, (2) two-class classification between ADHD and TD children using unbalanced data, (3) two-class classification between ADHDI and ADHDC, and (4) three-class classification among ADHDI, ADHDC, and TD children. Using Skip-Vote-Net, we achieved mean classification accuracies of 97% ± 1.87 and 97.7% ± 2.2 for the balanced and unbalanced classification cases, respectively. Furthermore, the mean classification accuracy for discriminating between ADHDI and ADHDC reached 99.4% ± 1.21. Finally, the proposed method demonstrated an average accuracy of 98.86% ± 1.03 in classifying ADHDI, ADHDC, and TD children collectively. Our findings highlight the superior performance of Skip-Vote-Net over existing methods in the classification of ADHD, showcasing its potential as an effective diagnostic tool for identifying ADHD subtypes and distinguishing ADHD from typically developing children.

Subject-specific atlas for automatic brain tissue segmentation of neonatal magnetic resonance images.

Developing advanced systems for 3D brain tissue segmentation from neonatal magnetic resonance (MR) images is vital for newborn structural analysis. However, automatic segmentation of neonatal brain tissues is challenging due to smaller head size and inverted T1/T2 tissue contrast compared to adults. In this work, a subject-specific atlas based technique is presented for segmentation of gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) from neonatal MR images. It involves atlas selection, subject-specific atlas creation using random forest (RF) classifier, and brain tissue segmentation using the expectation maximization-Markov random field (EM-MRF) method. To increase the segmentation accuracy, different tissue intensity- and gradient-based features were used. Evaluation on 40 neonatal MR images (gestational age of 37-44 weeks) demonstrated an overall accuracy of 94.3% and an average Dice similarity coefficient (DSC) of 0.945 (GM), 0.947 (WM), and 0.912 (CSF). Compared to multi-atlas segmentation methods like SEGMA and EM-MRF with multiple atlases, our method improved accuracy by up to 4%, particularly in complex tissue regions. Our proposed method allows accurate brain tissue segmentation, a crucial step in brain magnetic resonance imaging (MRI) applications including brain surface reconstruction and realistic head model creation in neonates.

Age-related differences in structural and resting-state functional brain network organization across the adult lifespan: A cross-sectional study.

We investigated age-related trends in the topology and hierarchical organization of brain structural and functional networks using diffusion-weighted imaging and resting-state fMRI data from a large cohort of healthy aging adults. At the cross-modal level, we explored age-related patterns in the RC involvement of different functional subsystems using a high-resolution functional parcellation. We further assessed age-related differences in the structure-function coupling as well as the network vulnerability to damage to rich club connectivity. Regardless of age, the structural and functional brain networks exhibited a rich club organization and small-world topology. In older individuals, we observed reduced integration and segregation within the frontal-occipital regions and the cerebellum along the brain's medial axis. Additionally, functional brain networks displayed decreased integration and increased segregation in the prefrontal, centrotemporal, and occipital regions, and the cerebellum. In older subjects, structural networks also exhibited decreased within-network and increased between-network RC connectivity. Furthermore, both within-network and between-network RC connectivity decreased in functional networks with age. An age-related decline in structure-function coupling was observed within sensory-motor, cognitive, and subcortical networks. The structural network exhibited greater vulnerability to damage to RC connectivity within the language-auditory, visual, and subcortical networks. Similarly, for functional networks, increased vulnerability was observed with damage to RC connectivity in the cerebellum, language-auditory, and sensory-motor networks. Overall, the network vulnerability decreased significantly in subjects older than 70 in both networks. Our findings underscore significant age-related differences in both brain functional and structural RC connectivity, with distinct patterns observed across the adult lifespan.

Neuroimaging determinants of cognitive impairment in the memory clinic: how important is the vascular burden?

While neurodegenerative and vascular neurocognitive disorder (NCD) often co-occur, the contribution of vascular lesions, especially stroke lesions identified on MRI, to global cognition in a real-life memory clinic population remains unclear. The main objective of this retrospective study was to determine NCD neuroimaging correlates: the GM atrophy pattern and vascular lesions (especially stroke lesion localization by voxel-based lesion-symptom mapping, VLSM) in a memory clinic. We included 336 patients with mild or major NCD who underwent cerebral MRI and a neuropsychological assessment. The GM atrophy pattern (obtained by voxel-based morphometry, VBM) and the stroke lesion localization (obtained by VLSM) associated with G5 z-score (a global cognitive score), were included as independent variables with other neuroimaging and clinical indices in a stepwise linear regression model. The mean age was 70.3 years and the mean MMSE score 21.3. On MRI, 75 patients had at least one stroke lesion. The G 5 z-score was associated with GM density in the pattern selected by the VBM analysis (R2 variation = 0.166, p < 0.001) and the presence of a stroke lesion in the region selected by the VSLM analysis (mainly in the right frontal region; R2 variation = 0.018, p = 0.008). The interaction between the two factors was insignificant (p = 0.374). In conclusion, in this first study combining VBM and VLSM analysis in a memory clinic, global cognition was associated with a specific GM atrophy pattern and the presence of a stroke lesion mainly in the right frontal region.

Flexible-Center Hat Complete Electrode Model for EEG Forward Problem.

OBJECTIVE: This study aims to develop a more realistic electrode model by incorporating the non-uniform distribution of electrode contact conductance (ECC) and the shunting effects, to accurately solve EEG forward problem (FP). METHODS: Firstly, a hat function is introduced to construct a more realistic hat-shaped distribution (HD) for ECC. Secondly, this hat function is modified by applying two parameters - offset ratio and offset direction - to account for the variability in ECC's center and to develop the flexible-center HD (FCHD). Finally, by integrating this FCHD into the complete electrode model (CEM) with the shunting effects, a novel flexible-center hat complete electrode model (FCH-CEM) is proposed and used to solve FP. RESULTS: Simulation experiments using a realistic head model demonstrate the necessity of FCH-CEM and its potential to improve the accuracy of the FP solution compared to current models, i.e., the point electrode model (PEM) and CEM. And compared to PEM, it has better performance under coarse mesh conditions (2 mm). Further experiments indicate the significance of considering shunting effects, as ignoring them results in larger errors than coarse mesh when the average contact conductance is large (101S/m2). CONCLUSION: The proposed FCH-CEM has better accuracy and performance than PEM and complements CEM in finer meshes, making it necessary for coarse meshes. SIGNIFICANCE: This study proposes a novel model that enhances electrode modeling and FP accuracy, and provides new ideas and methods for future research.

Are we ready to cure post-stroke cognitive impairment? Many key prerequisites can be achieved quickly and easily.

PURPOSE: Post-stroke (PS) cognitive impairment (CI) is frequent and its devastating functional and vital consequences are well known. Despite recent guidelines, they are still largely neglected. A large number of recent studies have re-examined the epidemiology, diagnosis, imaging determinants and management of PSCI. The aim of this update is to determine whether these new data answer the questions that are essential to reducing PSCI, the unmet needs, and steps still to be taken. METHODS: Literature review of stroke unit-era studies examining key steps in the management of PSCI: epidemiology and risk factors, diagnosis (cognitive profile and assessments), imaging determinants (quantitative measures, voxelwise localization, the disconnectome and associated Alzheimer's disease [AD]) and treatment (secondary prevention, symptomatic drugs, rehabilitation and noninvasive brain stimulation) of PSCI. FINDINGS: (1) the prevalence of PSCI of approximately 50% is probably underestimated; (2) the sensitivity of screening tests should be improved to detect mild PSCI; (3) comprehensive assessment is now well-defined and should include apathy; (4) easily available factors can identify patients at high risk of PSCI; (5) key imaging determinants are the location and volume of the lesion and the resulting disconnection, associated AD and brain atrophy; WMH, ePVS, microhemorrhages, hemosiderosis, and cortical microinfarcts may contribute to cognitive impairment but are more likely to be markers of brain vulnerability or associated AD that reduce PS recovery; (6) remote and online assessment is a promising approach for selected patients; (7) secondary stroke prevention has not been proven to prevent PSCI; (8) symptomatic drugs are ineffective in treating PSCI and apathy; (9) in addition to cognitive rehabilitation, the benefits of training platforms and computerized training are yet to be documented; (10) the results and the magnitude of improvement of noninvasive brain stimulation, while very promising, need to be substantiated by large, high-quality, sham-controlled RCTs. DISCUSSION AND CONCLUSION: These major advances pave the way for the reduction of PSCI. They include (1) the development of more sensitive screening tests applicable to all patients and (2) online remote assessment; crossvalidation of (3) clinical and (4) imaging factors to (5) identify patients at risk, as well as (6) factors that prompt a search for associated AD; (7) the inclusion of cognitive outcome as a secondary endpoint in acute and secondary stroke prevention trials; and (8) the validation of the benefit of noninvasive brain stimulation through high-quality, randomized, sham-controlled trials. Many of these objectives can be rapidly and easily attained.

Architecture and anatomy of executive processes: evidence from verbal fluency and Trail Making Test in 2009 stroke patients.

OBJECTIVES: The few voxel-wise lesion-symptom mapping (VLSM) studies aimed at identifying the anatomy of executive function are limited by the absence of a model and by small populations. Using Trail Making Test (TMT) and verbal fluency and a model of their architectures, our objective was to identify the key structures underlying two major executive processes, set-shifting and strategic word search. METHODS: We applied a validated VLSM analysis to harmonized cognitive and imaging data from 2009 ischemic stroke patients as a part of the Meta VCI Map consortium. All contrast analyses used an adjusted threshold with 2000 Freedman-Lane permutations (p ≤ 0.05). RESULTS: The TMT parts A and B were associated with structures involved in visual-spatial processing, the motor system, the frontal lobes, and their subcortical connections. Set-shifting depended on the left dorsomedial frontal region. Both semantic and phonemic fluency tests depended on verbal output abilities and processing speed with similar slopes in different languages. The strategic search process depended on Broca's area, F2 and related tracts, temporal and deep regions. Lastly, the lesion map of set-shifting did not overlap with those of strategic word search processes. INTERPRETATION: Our results identify the anatomical substrates of two main executive processes, revealing that they represent only a specific subpart of previously reported structures. Finally, our results indicate that executive functions depend on several specific, anatomically separable executive processes mainly operating in various parts of the frontal lobes.

Altered brain structural and functional connectivity in cannabis users.

Cannabis is one of the most used and commodified illicit substances worldwide, especially among young adults. The neurobiology mechanism of cannabis is yet to be identified particularly in youth. The purpose of this study was to concurrently measure alterations in brain structural and functional connectivity in cannabis users using resting-state functional magnetic resonance images (rs-fMRI) and diffusion-weighted images (DWI) from a group of 73 cannabis users (age 22-36, 19 female) in comparison with 73 healthy controls (age 22-36, 14 female) from Human Connectome Project (HCP). Several significant differences were observed in local structural/functional network measures (e.g. degree and clustering coefficient), being prominent in the insular and frontal opercular cortex and lateral/medial temporal cortex. The rich-club organization of structural networks revealed a normal trend, distributed within bilateral frontal, temporal and occipital regions. However, minor differences were found between the two groups in the superior and inferior temporal gyri. Functional rich-club nodes were mostly located within parietal and posterior areas, with minor differences between the groups found mainly in the centro-temporal and parietal regions. Regional network measures of structural/functional networks were associated with times used cannabis (TUC) in several regions. Although the structural/functional network in both groups showed small-world property, no differences between cannabis users and healthy controls were found regarding the global network measures, showing no association with cannabis use. After FDR correction, all of the significant associations between network measures and TUC were found to be insignificant, except for the association between degree and TUC within the presubiculum region. To recap, our findings revealed alterations in local topological properties of structural and functional networks in cannabis users, although their global brain network organization remained intact.

Poststroke apathy: Major role of cognitive, depressive and neurological disorders over imaging determinants.

Apathy occurs in approximately one third of people after stroke. Despite its frequency and functional consequences, the determinants of apathy have only been partially defined. The major difficulty lies in disentangling the reduction in activity due to apathy itself from those secondary to comorbidities, such as depression, sensorimotor deficits, and cognitive impairment. Here, we aimed to examine the prevalence of apathy, identify confounding sources of hypoactivity, and define its neuroimaging determinants using multivariate voxel lesion symptom-mapping (mVLSM) analyses. We assessed apathy in a subgroup (n = 325, mean age: 63.8 ± 10.5 years, 91.1% ischemic stroke) of the GRECogVASC cohort using the validated Behavioral Dysexecutive Syndrome Inventory, interpreted using GREFEX criteria, as well as confounding factors (depression, anxiety, severity of the neurological deficit, and gait disorders). mVLSM analysis was used to define neuroimaging determinants and was repeated after controlling for confounding factors. Apathy was present for 120 patients (36.9%, 95% CI: 31.7-42.2). Stepwise linear regression identified three factors associated with apathy: depressive symptoms (R2 = .3, p = .0001), cognitive impairment (R2 = .015, p = .02), and neurological deficit (R2 = .110, p = .0001). Accordingly, only 9 (7.5%) patients had apathy without a confounding factor, i.e., isolated apathy. In conventional VLSM analysis, apathy was associated with a large number of subcortical lesions that were no longer considered after controlling for confounding factors. Strategic site analysis identified five regions associated with isolated apathy: the F3 orbitalis pars, left amygdala, left thalamus, left pallidum, and mesencephalon. mVLSM analysis identified four strategic sites associated with apathy: the right corticospinal tract (R2 = .11; p = .0001), left frontostriatal tract (R2 = .11; p = .0001), left thalamus (R2 = .04; p = .0001), and left amygdala (R2 = .01; p = .013). These regions remained significant after controlling for confounding factors but explained a lower amount of variance. These findings indicate that poststroke apathy is more strongly associated with depression, neurological deficit, and cognitive impairment than with stroke lesions locations, at least using VLSM analysis.

Functional architecture of executive processes: Evidence from verbal fluency and lesion mapping in stroke patients.

The functional organization and related anatomy of executive functions are still largely unknown and were examined in the present study using a verbal fluency task. The objective of this study was to determine the cognitive architecture of a fluency task and related voxelwise anatomy in the GRECogVASC cohort and fMRI based meta-analytical data. First, we proposed a model of verbal fluency in which two control processes, lexico-semantic strategic search process and attention process, interact with semantic and lexico-phonological output processes. This model was assessed by testing 404 patients and 775 controls for semantic and letter fluency, naming, and processing speed (Trail Making test part A). Regression (R2 = .276 and .3, P = .0001, both) and structural equation modeling (CFI: .88, RMSEA: .2, SRMR: .1) analyses supported this model. Second, voxelwise lesion-symptom mapping and disconnectome analyses demonstrated fluency to be associated with left lesions of the pars opercularis, lenticular nucleus, insula, temporopolar region, and a large number of tracts. In addition, a single dissociation showed specific association of letter fluency with the pars triangularis of F3. Disconnectome mapping showed the additional role of disconnection of left frontal gyri and thalamus. By contrast, these analyses did not identify voxels specifically associated with lexico-phonological search processes. Third, meta-analytic fMRI data (based on 72 studies) strikingly matched all structures identified by the lesion approach. These results support our modeling of the functional architecture of verbal fluency based on two control processes (strategic search and attention) operating on semantic and lexico-phonologic output processes. Multivariate analysis supports the prominent role of the temporopolar area (BA 38) in semantic fluency and the F3 triangularis area (BA 45) in letter fluency. Finally, the lack of voxels specifically dedicated to strategic search processes could be due to a distributed organization of executive functions warranting further studies.

Brain Dynamics and Connectivity from Birth through Adolescence.

The human brain as a complex dynamic system undergoes significant structural and functional changes from birth to adulthood to engender neurocognitive functions [...].

Clinical and Imaging Determinants of Neurocognitive Disorders in Post-Acute COVID-19 Patients with Cognitive Complaints.

BACKGROUND: Neurocognitive disorders (NCDs) are a part of the post-acute coronavirus disease (COVID-19) syndrome. No study has specifically evaluated NCDs in post-acute COVID-19 patients with cognitive complaints or their MRI determinants. OBJECTIVE: To characterize NCDs in post-acute COVID-19 patients with cognitive complaints. The secondary objectives were to assess their clinical and MRI determinants. METHODS: We included 46 patients with a post-acute COVID-19 cognitive complaint referred to the Amiens University Hospital Memory Center. They underwent a neuropsychological assessment and 36 had cerebral MRI. The G3 overall summary score was the sum of the mean z scores for the executive function, language, and action speed domains. Neuropsychological profiles were compared in a general linear model. Clinical determinants were analyzed by stepwise linear regression. White matter hyperintensities (WMH) masks were analyzed using parcel-based WMH symptom mapping to identify the locations of WMHs associated with cognitive performance. RESULTS: Repeated ANOVA showed a group effect (p = 0.0001) due to overall lower performance for patients and a domain effect (p = 0.0001) due to a lower (p = 0.007) action speed score. The G3 overall summary score was significantly associated with solely the requirement for oxygen (R2 = 0.319, p = 0.031). WHMs were associated with the G3 overall summary score in the following structures, all right-sided (p < 0.01): superior frontal region, postcentral region, cingulum, cortico-spinal tract, inferior longitudinal fasciculus, internal capsule, and posterior segment of the arcuate fasciculus. CONCLUSION: Post-acute COVID-19 patients with cognitive complaints had NCD, with prominent action slowing, significantly associated with the acute phase oxygen requirement and a right-sided WMH structure pattern.

Temporally dynamic neural correlates of drug cue reactivity, response inhibition, and methamphetamine-related response inhibition in people with methamphetamine use disorder.

Cue-induced drug craving and disinhibition are two essential components of continued drug use and relapse in substance use disorders. While these phenomena develop and interact across time, the temporal dynamics of their underlying neural activity remain under-investigated. To explore these dynamics, an analysis of time-varying activation was applied to fMRI data from 62 men with methamphetamine use disorder in their first weeks of recovery in an abstinence-based treatment program. Using a mixed block-event, factorial cue-reactivity/Go-NoGo task and a sliding window across the task duration, dynamically-activated regions were identified in three linear mixed effects models (LMEs). Habituation to drug cues across time was observed in the superior temporal gyri, amygdalae, left hippocampus, and right precuneus, while response inhibition was associated with the sensitization of temporally-dynamic activations across many regions of the inhibitory frontoparietal network. Methamphetamine-related response inhibition was associated with temporally-dynamic activity in the parahippocampal gyri and right precuneus (corrected p-value < 0.001), which show a declining cue-reactivity contrast and an increasing response inhibition contrast. Overall, the declining craving-related activations (habituation) and increasing inhibition-associated activations (sensitization) during the task duration suggest the gradual recruitment of response inhibitory processes and a concurrent habituation to drug cues in areas with temporally-dynamic methamphetamine-related response inhibition. Furthermore, temporally dynamic cue-reactivity and response inhibition were correlated with behavioral and clinical measures such as the severity of methamphetamine use and craving, impulsivity and inhibitory task performance. This exploratory study demonstrates the time-variance of the neural activations undergirding cue-reactivity, response inhibition, and response inhibition during exposure to drug cues, and suggests a method to assess this dynamic interplay. Analyses that can capture temporal fluctuations in the neural substrates of drug cue-reactivity and response inhibition may prove useful for biomarker development by revealing the rate and pattern of sensitization and habituation processes, and may inform mixed cue-exposure intervention paradigms which could promote habituation to drug cues and sensitization in inhibitory control regions.

Poststroke action slowing: Motor and attentional impairments and their imaging determinants. Evidence from lesion-symptom mapping, disconnection and fMRI activation studies.

BACKGROUND AND OBJECTIVES: Although action slowing is the main cognitive impairment in stroke survivors, its mechanisms and determinants are still poorly understood. The objectives of the present study were to determine the mechanisms of post-stroke action slowing (using validated, highly specific simple reaction time (SRT) and tapping tests) and identify its imaging determinants (using multivariate lesion-symptom mapping (mLSM)). METHODS: Action speed in the GRECogVASC cohort was assessed using finger tapping and SRT tests performed with both hands and analyzed using previously validated indices. Imaging determinants were identified using validated mLSM analyses and disconnection analysis and compared to those of an fMRI activation meta-analytic database. RESULTS: Both the tapping time and SRT were 10.7% slower for the 394 patients (p = 0.0001) than for the 786 controls, without a group × test interaction (p = 0.2). The intra-individual distribution curve was characterized by a rightward shift with an unaltered attentional peak. The mLSM analyses showed tapping to be associated with lesions in the frontostriatal tract (p = 0.0007). The SRT was associated with lesions in the frontostriatal tract (p = 0.04) and the orbital part of F3 (p = 0.0001). The SRT-tapping index was associated with lesions in the orbital part of F3 (p = 0.0001). All lesions were located in the right hemisphere only and were responsible for the disconnection of several structures involved in motor preparation, initiation, and speed. A comparison with fMRI activation meta-analytic data highlighted mostly the same regions, including the orbital part of F3, the ventral and dorsal parts of F1, and the premotor and cingulate regions in the right hemisphere. DISCUSSION: Our results confirm the marked impairment of action speed in stroke and show that the primary mechanism is motor slowing and that it is related to lesions in the right frontostriatal tract. A deficit in sustained alertness accounted for action slowing in the subgroup with lesions in the right orbital part of F3. Our SRT and mLSM results were in accordance with the fMRI activation data. Thus, stroke induces slowing in the broad network associated with SRT tasks by disrupting the frontostriatal tract and, to a lesser extent, other sites involved in attention.