Mapping human brain lesions and their functional consequences

Mapping Lesion-Related Human Aggression to a Common Brain Network

BACKGROUND

Aggression exacts a significant toll on human societies and is highly prevalent among neuropsychiatric patients. The neural mechanisms of aggression are unclear and treatment options are limited.

METHODS

Using a recently validated lesion network mapping technique, we derived an aggression-associated network by analyzing data from 182 patients who had experienced penetrating head injuries during their service in the Vietnam War. To test whether damage to this lesion-derived network would increase the risk of aggression-related neuropsychiatric symptoms, we used the Harvard Lesion Repository (N = 852). To explore potential therapeutic relevance of this network, we used an independent deep brain stimulation dataset of 25 patients with epilepsy, in which irritability and aggression are known potential side effects.

RESULTS

We found that lesions associated with aggression occurred in many different brain locations but were characterized by a specific brain network defined by functional connectivity to a hub region in the right prefrontal cortex. This network involves positive connectivity to the ventromedial prefrontal cortex, dorsolateral prefrontal cortex, frontal pole, posterior cingulate cortex, anterior cingulate cortex, temporal-parietal junction, and lateral temporal lobe and negative connectivity to the amygdala, hippocampus, insula, and visual cortex. Among all 24 neuropsychiatric symptoms included in the Harvard Lesion Repository, criminality demonstrated the most alignment with our aggression-associated network. Deep brain stimulation site connectivity to this same network was associated with increased irritability.

CONCLUSIONS

We conclude that brain lesions associated with aggression map to a specific human brain circuit, and the functionally connected regions in this circuit provide testable targets for therapeutic neuromodulation.

Mapping glioblastoma-induced neurological deficits: A brain atlas

BACKGROUND

Identifying radiological characteristics and brain regions associated with neurological deficits in glioblastoma patients can improve diagnostic evaluation and understanding of the disease's impact on neurological function.

METHODS

The retrospective study included 527 newly diagnosed glioblastoma patients. Eligibility criteria included pathologically confirmed IDH-wild type glioblastoma, availability of pre- and post-contrast MRIs, and detailed neurological examination reports. Contrast-enhancing tumors (CET) and non-contrast-enhancing lesions (NEL) were segmented from 3 Tesla MRI scans. Lesion volumes from patients without neurological deficits compared with symptomatic patients using either the Mann-Whitney test or Kruskal-Wallis test. Voxel-wise lesion-symptom mapping was conducted using Fisher-exact-test followed by random permutation analysis (ADIFFI) to identify brain regions with higher occurrences of deficit-associated lesions.

RESULTS

Location of CET and NEL within the brain were associated with specific neurological deficits. Larger CET and NEL volumes were associated with increased neurological deficits (CET: rs = 0.15, p = 0.0006; NEL: rs = 0.22, p < 0.0001). Lesion volumes were smaller in patients without neurological deficits (CET: 4.97 ± 0.69 ml vs. 20.0 ± 0.9 ml, p < 0.0001). Epilepsy-associated lesions were also smaller (CET: 4.59 ± 0.55 ml vs. 22.0 ± 0.9 ml, p < 0.0001).

CONCLUSION

The study highlights that neurological and epilepsy status at pre-treatment provide estimates of glioblastoma lesion volumes and locations. The correlation between lesion volumes and neurological deficits underscores the significance of comprehensive radiological assessments in glioblastoma patients. These findings support the use of detailed lesion-symptom mapping to guide clinical management and prognosis evaluation in glioblastoma.

The Journey of the Default Mode Network: Development, Function, and Impact on Mental Health

The Default Mode Network has been extensively studied in recent decades due to its central role in higher cognitive processes and its relevance for understanding mental disorders. This neural network, characterized by synchronized and coherent activity at rest, is intrinsically linked to self-reflection, mental exploration, social interaction, and emotional processing. Our understanding of the DMN extends beyond humans to non-human animals, where it has been observed in various species, highlighting its evolutionary basis and adaptive significance throughout phylogenetic history. Additionally, the DMN plays a crucial role in brain development during childhood and adolescence, influencing fundamental cognitive and emotional processes. This literature review aims to provide a comprehensive overview of the DMN, addressing its structural, functional, and evolutionary aspects, as well as its impact from infancy to adulthood. By gaining a deeper understanding of the organization and function of the DMN, we can advance our knowledge of the neural mechanisms that underlie cognition, behavior, and mental health. This, in turn, can lead to more effective therapeutic strategies for a range of neuropsychiatric conditions.

Visual feature processing in a large stroke cohort: evidence against modular organization

Mid-level visual processing represents a crucial stage between basic sensory input and higher-level object recognition. The conventional model posits that fundamental visual qualities, such as colour and motion, are processed in specialized, retinotopic brain regions (e.g. V4 for colour, MT/V5 for motion). Using atlas-based lesion-symptom mapping and disconnectome maps in a cohort of 307 ischaemic stroke patients, we examined the neuroanatomical correlates underlying the processing of eight mid-level visual qualities. Contrary to the predictions of the standard model, our results did not reveal consistent relationships between processing impairments and damage to traditionally associated brain regions. Although we validated our methodology by confirming the established relationship between visual field defects and damage to primary visual areas (V1, V2 and V3), we found no reliable evidence linking processing deficits to specific regions in the posterior brain. These findings challenge the traditional modular view of visual processing and suggest that mid-level visual processing might be more distributed across neural networks than previously thought. This supports alternative models where visual maps represent constellations of co-occurring information rather than specific qualities.

Voxel-Based Lesion Symptom Mapping to Predict Poststroke Epilepsy After Mechanical Thrombectomy

INTRODUCTION

Poststroke epilepsy (PSE) is an important long-term complication after stroke. Data regarding predictors of PSE in patients with large-vessel occlusion stroke receiving mechanical thrombectomy (MT) are scarce. Voxel-based lesion symptom mapping on brain MRI might be a valuable tool in the risk prediction of PSE. This study aims to assess PSE risk after acute stroke treated with MT via voxel- and volumetric-based analyses.

METHODS

In this bi-center study from two tertiary-care stroke centers, we included consecutive acute ischemic stroke patients who had received MT between 2011 and 2017, and had postinterventional brain MRI as well as long-term follow-up data available. Infarct volume and location were assessed on MRI. Following semiautomated lesion outlining and generation of binarized lesion masks, lesion symptom mapping was applied to identify relevant topographical lesion patterns in PSE.

RESULTS

Of 348 analyzed patients, 97 cases had to be excluded due to insufficient image quality and inaccurate registration results. Finally, lesion maps from 251 patients (median age: 66, 45.4% women) were considered for lesion symptom mapping, including maps from 26 patients with PSE (10.4%). Mean infarct volume was higher in PSE patients (119.2 cm3 vs. 43.9 cm3, p < 0.0001). Lesion symptom mapping identified the orbitofrontal region, the operculum, and the temporal pole as brain regions associated with PSE.

CONCLUSION

Apart from infarct volume, lesion symptom mapping on postinterventional brain MRI identified specific brain regions associated with PSE after large vessel occlusion stroke. This information might be helpful for PSE risk stratification and follow-up care in this specific population.

Functional magnetic resonance imaging in prurigo nodularis: A call to study neural sensitization phenomena

Prurigo nodularis is a chronic pruritic, inflammatory skin condition characterized by nodular skin lesions in a typical distribution pattern caused by various dermatologic and/or nondermatologic conditions. In recent years, significant advances have been made in the understanding of the cutaneous pathophysiology of prurigo nodularis, resulting in novel treatment options such as interleukin-4, -13, -17, and -31 or Janus kinase inhibitors. Many aspects of the neurophysiology are largely unknown, including the processing in the central structural and functional network involved in prurigo nodularis. Functional neuroimaging allows noninvasive assessment of brain function and structure. Due to its high spatial resolution and temporal precision, functional magnetic resonance imaging has proven to be a suitable method for exploring neural mechanisms and assessing pharmacologic effects in dermatologic research. In this systematic review, the current knowledge of functional magnetic resonance imaging in the context of prurigo nodularis and its centrally active treatment options is summarized.

What I think she thinks about my paralysed body: Social inferences about disability-related content in anosognosia for hemiplegia

The neuropsychological disorder of anosognosia for hemiplegia (AHP) can offer unique insights into the neurocognitive processes of body consciousness and representation. Previous studies have found associations between selective social cognition deficits and anosognosia. In this study, we examined how such social cognition deficits may directly interact with representations of one's body as disabled in AHP. We used a modified set of previously validated Theory of Mind (ToM) stories to create disability-related content that was related to post-stroke paralysis and to investigate differences between right hemisphere damage patients with (n = 19) and without (n = 19) AHP. We expected AHP patients to perform worse than controls when trying to infer paralysis-related mental states in the paralysis-related ToM stories and explored whether such differences depended on the inference patients were asked to perform (e.g. self or other referent perspective-taking). Using an advanced structural neuroimaging technique, we expected selective social cognitive deficits to be associated with posterior parietal cortex lesions and deficits in self-referent perspective-taking in paralysis-related mentalising to be associated with frontoparietal disconnections. Group- and individual-level results revealed that AHP patients performed worse than HP controls when trying to infer paralysis-related mental states. Exploratory lesion analysis results revealed some of the hypothesised lesions, but also unexpected white matter disconnections in the posterior body and splenium of the corpus collosum associated with a self-referent perspective-taking in paralysis-related ToM stories. The study has implications for the multi-layered nature of body awareness, including abstract, social perspectives and beliefs about the body.

Time perception in cerebellar and basal ganglia stroke patients

The neural mechanisms underlying time perception remain elusive. Although the cerebellum (CE) and basal ganglia (BG) are considered fundamental, evidence primarily stems from studies on neurodegenerative diseases, where progressive and widespread damage complicates linking deficits to specific brain structures. In contrast, brain stroke affects focal areas suddenly, allowing for the assessment of immediate functional consequences. Here, we compared patients with acute stroke in the CE and BG to age-matched healthy controls (HC) on both explicit (time bisection, free and 1-second finger tapping) and implicit (rhythmic, temporal orienting) timing tasks. Concerning explicit timing, both CE and BG patients were faster than HC in their free finger tapping, while BG lesions showed greater variability than HC in the 1-second tapping. Similarly, performance on the bisection task suggested deficits more related to cognitive complaints in stroke than specific temporal dysfunction. In implicit timing tasks, BG patients, like HC, effectively used information provided by the rhythm and the temporal orienting cues to anticipate the target onset, whereas CE patients failed and showed longer reaction times. Therefore, before compensatory mechanisms can take effect, acute CE damage might hinder implicit timing, whereas BG lesions could disrupt explicit temporal representation when processed alongside other cognitive functions.

Network-based statistics distinguish anomic and Broca's aphasia

INTRODUCTION

Aphasia is a speech-language impairment commonly caused by damage to the left hemisphere. The neural mechanisms that underpin different types of aphasia and their symptoms are still not fully understood. This study aims to identify differences in resting-state functional connectivity between anomic and Broca's aphasia measured through resting-state functional magnetic resonance imaging (rs-fMRI).

METHODS

We used the network-based statistic (NBS) method, as well as voxel- and connectome-based lesion symptom mapping (V-, CLSM), to identify distinct neural correlates of the anomic and Broca's groups. To control for lesion effect, we included lesion volume as a covariate in both the NBS method and LSM.

RESULTS

NBS identified a subnetwork located in the dorsal language stream bilaterally, including supramarginal gyrus, primary sensory, motor, and auditory cortices, and insula. The connections in the subnetwork were weaker in the Broca's group than the anomic group. The properties of the subnetwork were examined through complex network measures, which indicated that regions in right inferior frontal sulcus, right paracentral lobule, and bilateral superior temporal gyrus exhibit intensive interaction. Left superior temporal gyrus, right postcentral gyrus, and left supramarginal gyrus play an important role in information flow and overall communication efficiency. Disruption of this network underlies the constellation of symptoms associated with Broca's aphasia. Whole-brain CLSM did not detect any significant connections, suggesting an advantage of NBS when thousands of connections are considered. However, CLSM identified connections that differentiated Broca's from anomic aphasia when analysis was restricted to a hypothesized network of interest.

DISCUSSION

We identified novel signatures of resting-state brain network differences between groups of individuals with anomic and Broca's aphasia. We identified a subnetwork of connections that statistically differentiated the resting-state brain networks of the two groups, in comparison with standard CLSM results that yielded isolated connections. Network-level analyses are useful tools for the investigation of the neural correlates of language deficits post-stroke.

Heterogenous brain activations across individuals localize to a common network

Task functional magnetic resonance imaging research has generally shielded away from studying individuals due to the low reproducibility. Here, we propose that heterogeneous brain activations across individuals localize to a common network. To test this hypothesis, we use working memory (WM) as our example. First, we showed that discrete-brain-based reproducibility of brain activation during WM across individuals was low. Then, we used activation network mapping (ANM) technique to identify each individual's brain network of WM and found that network-based reproducibility was rather high. Prediction analyses using machine learning algorithms indicated that individual WM networks identified via ANM can predict WM behavioral performance. This predictive ability even outperformed that of brain activations. Our study provides a new explanation on the low reproducibility of brain activations across individuals. The results suggest that ANM can be used to identify individual brain networks of cognitive processes, thus promising broad potential applications.

A Rose by Any Other Name: Mapping Taxonomic and Thematic Naming Errors Poststroke

Understanding the neurobiology of semantic knowledge is a major goal of cognitive neuroscience. Taxonomic and thematic semantic knowledge are represented differently within the brain's conceptual networks, but the specific neural mechanisms remain unclear. Some neurobiological models propose that the anterior temporal lobe is an important hub for taxonomic knowledge, whereas the TPJ is especially involved in the representation of thematic knowledge. However, recent studies have provided divergent evidence. In this context, we investigated the neural correlates of taxonomic and thematic confrontation naming errors in 79 people with aphasia. We used three complementary lesion-symptom mapping (LSM) methods to investigate how structure and function in both spared and impaired brain regions relate to taxonomic and thematic naming errors. Voxel-based LSM mapped brain damage, activation-based LSM mapped BOLD signal in surviving tissue, and network-based LSM mapped white matter subnetwork integrity to error type. Voxel- and network-based lesion symptom mapping provided converging evidence that damage/disruption of the left mid-to-anterior temporal lobe was associated with a greater proportion of thematic naming errors. Activation-based lesion symptom mapping revealed that higher BOLD signal in the left anterior temporal lobe during an in-house naming task was associated with a greater proportion of taxonomic errors on the Philadelphia Naming Test administered outside of the scanner. A lower BOLD signal in the bilateral angular gyrus, precuneus, and right inferior frontal cortex was associated with a greater proportion of taxonomic errors. These findings provide novel evidence that damage to the anterior temporal lobe is especially related to thematic naming errors.

Symptomatology after damage to the angular gyrus through the lenses of modern lesion-symptom mapping

Brain-behavior relationships are complex. For instance, one might know a brain region's function(s) but still be unable to accurately predict deficit type or severity after damage to that region. Here, I discuss the case of damage to the angular gyrus (AG) that can cause left-right confusion, finger agnosia, attention deficit, and lexical agraphia, as well as impairment in sentence processing, episodic memory, number processing, and gesture imitation. Some of these symptoms are grouped under AG syndrome or Gerstmann's syndrome, though its exact underlying neuronal systems remain elusive. This review applies recent frameworks of brain-behavior modes and principles from modern lesion-symptom mapping to explain symptomatology after AG damage. It highlights four major issues for future studies: (1) functionally heterogeneous symptoms after AG damage need to be considered in terms of the degree of damage to (i) different subdivisions of the AG, (ii) different AG connectivity profiles that disconnect AG from distant regions, and (iii) lesion extent into neighboring regions damaged by the same infarct. (2) To explain why similar symptoms can also be observed after damage to other regions, AG damage needs to be studied in terms of the networks of regions that AG functions with, and other independent networks that might subsume the same functions. (3) To explain inter-patient variability on AG symptomatology, the degree of recovery-related brain reorganisation needs to account for time post-stroke, demographics, therapy input, and pre-stroke differences in functional anatomy. (4) A better integration of the results from lesion and functional neuroimaging investigations of AG function is required, with only the latter so far considering AG function in terms of a hub within the default mode network. Overall, this review discusses why it is so difficult to fully characterize the AG syndrome from lesion data, and how this might be addressed with modern lesion-symptom mapping.

Localization and Network Connectivity of Lesions Causing Limb Ataxia in Patients With Stroke

BACKGROUND AND OBJECTIVES

Ataxia is primarily considered to originate from the cerebellum. However, it can manifest without obvious cerebellar damage, such as in anterior circulation stroke, leaving the mechanisms of ataxia unclear. The aim of this study was to investigate whether stroke lesions causing limb ataxia localize to a common brain network.

METHODS

In this prospective cohort study, adult patients with new-onset stroke with visible lesions on CT or MRI from Turku University Hospital, Finland, were clinically examined (1) after their stroke while still admitted to the hospital (baseline) and (2) 4 months later (follow-up) to assess limb ataxia. Lesion locations and their functional connectivity, computed using openly available data from 1,000 healthy volunteers from the Brain Genome Superstruct Project, were compared voxel-by-voxel across the whole brain between patients with and without ataxia, using voxel-based lesion-symptom mapping and lesion network mapping. The findings were confirmed in an independent stroke patient cohort with identical clinical assessments.

RESULTS

One hundred ninety-seven patients (mean age 67.2 years, 39%female) were included in this study. At baseline, 35 patients (68.3 years, 34%female) had and 162 (67.0 years, 40%female) did not have new-onset acute limb ataxia. At follow-up, additional 4 patients had developed late-onset limb ataxia, totalling to 39 patients (68.6 years, 36%female) with limb ataxia at any point. One hundred eighteen patients (66.2 years, 40%female) did not have ataxia at any point (n = 40 with missing follow-up data). Lesions in 54% of the patients with acute limb ataxia were located outside the cerebellum and cerebellar peduncles, and we did not find an association between specific lesion locations and ataxia. Lesions causing acute limb ataxia, however, were connected to a common network centered on the intermediate zone cerebellum and cerebellar peduncles (lesion connectivity in patients with vs without acute limb ataxia, pFWE < 0.05). The results were similar when comparing patients with and without ataxia at any point, and when excluding lesions in the cerebellum and cerebellar peduncles (pFWE < 0.05). The findings were confirmed in the independent stroke dataset (n = 96), demonstrating an OR of 2.27 (95% CI 1.32-3.91) for limb ataxia per standard deviation increase in limb ataxia network damage score.

DISCUSSION

Lesions causing limb ataxia occur in heterogeneous locations but localize to a common brain network.

The Aphasia Recovery Cohort, an open-source chronic stroke repository

Sharing neuroimaging datasets enables reproducibility, education, tool development, and new discoveries. Neuroimaging from many studies are publicly available, providing a glimpse into progressive disorders and human development. In contrast, few stroke studies are shared, and these datasets lack longitudinal sampling of functional imaging, diffusion imaging, as well as the behavioral and demographic data that encourage novel applications. This is surprising, as stroke is a leading cause of disability, and acquiring brain imaging is considered standard of care. The first release of the Aphasia Recovery Cohort includes imaging data, demographics and behavioral measures from 230 chronic stroke survivors who experienced aphasia. We also share scripts to illustrate how the imaging data can predict impairment. In conclusion, recent advances in machine learning thrive on large, diverse datasets. Clinical data sharing can contribute to improvements in automated detection of brain injury, identification of white matter hyperintensities, measures of brain health, and prognostic abilities to guide care.

Mapping stroke outcomes: A review of brain connectivity atlases

The brain connectivity-based atlas is a promising tool for understanding neural communication pathways in the brain, gaining relevance in predicting personalized outcomes for various brain pathologies. This critical review examines the robustness of the brain connectivity-based atlas for predicting post-stroke outcomes. A comprehensive literature search was conducted from 2012 to May 2023 across PubMed, Scopus, EMBASE, EBSCOhost, and Medline databases. Twenty-one studies were screened, and through analysis of these studies, we identified 18 brain connectivity atlases employed by the studies for lesion analysis in their predictions. The brain atlases were assessed for study cohorts, connectivity measures, identified brain regions, atlas applications, and limitations. Based on the analysis of these studies, most atlases were based on diffusion tensor imaging and resting-state functional magnetic resonance imaging (MRI). Studies predicting post-stroke functional outcomes relied on the atlases for multivariate lesion analysis and region of interest identification, often employing atlases derived from young, healthy populations. Current brain connectivity-based atlases for stroke applications lack standardized methods to define and map brain connectivity across atlases and cover sensorimotor functional connectivity to a limited extent. In conclusion, this review highlights the need to develop more comprehensive, robust, and adaptable brain connectivity-based atlases specifically tailored to post-stroke populations.

False Discovery Rate Control for Lesion-Symptom Mapping With Heterogeneous Data via Weighted p-Values

Lesion-symptom mapping studies provide insight into what areas of the brain are involved in different aspects of cognition. This is commonly done via behavioral testing in patients with a naturally occurring brain injury or lesions (e.g., strokes or brain tumors). This results in high-dimensional observational data where lesion status (present/absent) is nonuniformly distributed, with some voxels having lesions in very few (or no) subjects. In this situation, mass univariate hypothesis tests have severe power heterogeneity where many tests are known a priori to have little to no power. Recent advancements in multiple testing methodologies allow researchers to weigh hypotheses according to side information (e.g., information on power heterogeneity). In this paper, we propose the use of p-value weighting for voxel-based lesion-symptom mapping studies. The weights are created using the distribution of lesion status and spatial information to estimate different non-null prior probabilities for each hypothesis test through some common approaches. We provide a monotone minimum weight criterion, which requires minimum a priori power information. Our methods are demonstrated on dependent simulated data and an aphasia study investigating which regions of the brain are associated with the severity of language impairment among stroke survivors. The results demonstrate that the proposed methods have robust error control and can increase power. Further, we showcase how weights can be used to identify regions that are inconclusive due to lack of power.

Multivariate mapping of low-resilient neurocognitive systems within and around low-grade gliomas

Accumulating evidence suggests that the brain exhibits a remarkable capacity for functional compensation in response to neurological damage, a resilience potential that is deeply rooted in the malleable features of its underlying anatomofunctional architecture. This propensity is particularly exemplified by diffuse low-grade glioma, a subtype of primary brain tumour. However, functional plasticity is not boundless, and surgical resections directed at structures with limited neuroplasticity can lead to incapacitating impairments. Yet, maximizing diffuse low-grade glioma resections offers substantial oncological benefits, especially when the resection extends beyond the tumour margins (i.e. supra-tumour or supratotal resection). In this context, the primary objective of this study was to identify which cerebral structures were associated with less favourable cognitive outcomes after surgery, while accounting for intra-tumour and supra-tumour features of the surgical resections. To achieve this objective, we leveraged a unique cohort of 400 patients with diffuse low-grade glioma who underwent surgery with awake cognitive mapping. Patients benefitted from a neuropsychological assessment consisting of 18 subtests administered before and 3 months after surgery. We analysed changes in performance and applied topography-focused and disconnection-focused multivariate lesion-symptom mapping using support vector regressions, in an attempt to capture resected cortico-subcortical structures less amenable to full cognitive compensation. The observed changes in performance were of a limited magnitude, suggesting an overall recovery (13 of 18 tasks recovered fully despite a mean resection extent of 92.4%). Nevertheless, lesion-symptom mapping analyses revealed that a lack of recovery in picture naming was linked to damage in the left inferior temporal gyrus and inferior longitudinal fasciculus. Likewise, for semantic fluency abilities, an association was established with damage to the left precuneus/posterior cingulate. For phonological fluency abilities, the left dorsomedial frontal cortex and the frontal aslant tract were implicated. Moreover, difficulties in spatial exploration were associated with injury to the right dorsomedial prefrontal cortex and its underlying connectivity. An exploratory analysis suggested that supra-tumour resections were associated with a less pronounced recovery following specific resection patterns, such as supra-tumour resections of the left uncinate fasciculus (picture naming), the left corticostriatal tract and the anterior corpus callosum (phonological fluency), the hippocampus and parahippocampus (episodic memory) and the right frontal-mesial areas (visuospatial exploration). Collectively, these patterns of results shed new light on both low-resilient neural systems and the prediction of cognitive recovery following glioma surgery. Furthermore, they indicate that supra-tumour resections were only occasionally less well tolerated from a cognitive viewpoint. In doing so, they have deep implications for surgical planning and rehabilitation strategies.

Stable multivariate lesion symptom mapping

Multivariate lesion-symptom mapping (MLSM) considers lesion information across the entire brain to predict impairments. The strength of this approach is also its weakness-considering many brain features together synergistically can uncover complex brain-behavior relationships but exposes a high-dimensional feature space that a model is expected to learn. Successfully distinguishing between features in this landscape can be difficult for models, particularly in the presence of irrelevant or redundant features. Here, we propose stable multivariate lesion-symptom mapping (sMLSM), which integrates the identification of reliable features with stability selection into conventional MLSM and describe our open-source MATLAB implementation. Usage is showcased with our publicly available dataset of chronic stroke survivors (N=167) and further validated in our independent public acute stroke dataset (N = 1106). We demonstrate that sMLSM eliminates inconsistent features highlighted by MLSM, reduces variation in feature weights, enables the model to learn more complex patterns of brain damage, and improves model accuracy for predicting aphasia severity in a way that tends to be robust regarding the choice of parameters for identifying reliable features. Critically, sMLSM more consistently outperforms predictions based on lesion size alone. This advantage is evident starting at modest sample sizes (N>75). Spatial distribution of feature importance is different in sMLSM, which highlights the features identified by univariate lesion symptom mapping while also implicating select regions emphasized by MLSM. Beyond improved prediction accuracy, sMLSM can offer deeper insight into reliable biomarkers of impairment, informing our understanding of neurobiology.

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.

Heterogeneous Brain Atrophy Sites in Anxiety Disorders Map to a Common Brain Network

Background

Heterogeneous findings among anxiety disorder studies have hindered elucidation of the underlying pathophysiology and the development of mechanism-based therapies.

Purpose

To determine whether structural MRI findings in anxiety disorder studies converge on a common network with therapeutic significance.

Materials and Methods

In this retrospective study, a systematic literature search of PubMed and Web of Science databases was performed to identify coordinates of gray matter atrophy in patients with anxiety disorder. Atrophy coordinates were then mapped to an anxiety network constructed from the resting-state functional MRI (rs-fMRI) data of 652 healthy participants using "coordinate network mapping" and validated by specificity tests. The causal association of this network to anxiety symptoms was tested in a cohort of patients with brain lesions and emergent anxiety symptoms. The potential therapeutic utility of this anxiety network was then assessed by examining the clinical efficacy of network-targeted repetitive transcranial magnetic stimulation (rTMS) among a separate anxiety disorder cohort. Statistical analyses of images were performed using nonparametric tests and corrected for family-wise error.

Results

Sixteen studies comprising 453 patients with anxiety (245 females; mean age ± [SD], 31.4 ± 8.71 years) and 460 healthy controls (238 females; 31.7 ± 10.08 years) were included in the analysis. Atrophy coordinates were mapped to an anxiety network with a hub region situated primarily within the superficial amygdala. Lesions associated with emergent anxiety symptoms exhibited stronger connectivity within this anxiety network than lesions not associated with anxiety (t = 2.99; P = .004). Moreover, the connectivity strength of rTMS targets in the anxiety network was correlated with the improvements of anxiety symptom after treatment (r = .42, P = .02).

Conclusions

Heterogeneous gray matter atrophy among patients with anxiety disorder localize to a common network that may serve as an effective therapeutic target.

Effort-based decision making and motivational deficits in stroke patients

Motivational deficits in patients recovering from stroke are common and can reduce active participation in rehabilitation and thereby impede functional recovery. We investigated whether stroke patients with clinically reduced drive, initiation, and endurance during functional rehabilitative training (n = 30) display systematic alterations in effort-based decision making compared to age, sex, and severity-matched stroke patients (n = 30) whose drive appeared unaffected. Notably, the two groups did not differ in self-reported ratings of apathy and depression. However, on an effort-based decision-making task, stroke patients with clinically apparent drive impairment showed intact willingness to accept effort for reward, but were more likely to fail to execute the required effort compared to patients without apparent drive impairments. In other words, the decision behavioural assessment revealed that stroke patients that displayed reduced drive, initiation, and endurance during inpatient neurorehabilitation failed to persist in goal-directed effort production, even over very short periods. These findings indicate that reduced drive during rehabilitative therapy in post-stroke patients is not due to a diminished motivation to invest physical effort, but instead is related to a reduced persistence with effortful behaviour.

The neuroanatomy of visual extinction following right hemisphere brain damage: Insights from multivariate and Bayesian lesion analyses in acute stroke

Multi-target attention, that is, the ability to attend and respond to multiple visual targets presented simultaneously on the horizontal meridian across both visual fields, is essential for everyday real-world behaviour. Given the close link between the neuropsychological deficit of extinction and attentional limits in healthy subjects, investigating the anatomy that underlies extinction is uniquely capable of providing important insights concerning the anatomy critical for normal multi-target attention. Previous studies into the brain areas critical for multi-target attention and its failure in extinction patients have, however, produced heterogeneous results. In the current study, we used multivariate and Bayesian lesion analysis approaches to investigate the anatomical substrate of visual extinction in a large sample of 108 acute right hemisphere stroke patients. The use of acute stroke patient data and multivariate/Bayesian lesion analysis approaches allowed us to address limitations associated with previous studies and so obtain a more complete picture of the functional network associated with visual extinction. Our results demonstrate that the right temporo-parietal junction (TPJ) is critically associated with visual extinction. The Bayesian lesion analysis additionally implicated the right intraparietal sulcus (IPS), in line with the results of studies in neurologically healthy participants that highlighted the IPS as the area critical for multi-target attention. Our findings resolve the seemingly conflicting previous findings, and emphasise the urgent need for further research to clarify the precise cognitive role of the right TPJ in multi-target attention and its failure in extinction patients.

Emotion in action: When emotions meet motor circuits

The brain is a remarkably complex organ responsible for a wide range of functions, including the modulation of emotional states and movement. Neuronal circuits are believed to play a crucial role in integrating sensory, cognitive, and emotional information to ultimately guide motor behavior. Over the years, numerous studies employing diverse techniques such as electrophysiology, imaging, and optogenetics have revealed a complex network of neural circuits involved in the regulation of emotional or motor processes. Emotions can exert a substantial influence on motor performance, encompassing both everyday activities and pathological conditions. The aim of this review is to explore how emotional states can shape movements by connecting the neural circuits for emotional processing to motor neural circuits. We first provide a comprehensive overview of the impact of different emotional states on motor control in humans and rodents. In line with behavioral studies, we set out to identify emotion-related structures capable of modulating motor output, behaviorally and anatomically. Neuronal circuits involved in emotional processing are extensively connected to the motor system. These circuits can drive emotional behavior, essential for survival, but can also continuously shape ongoing movement. In summary, the investigation of the intricate relationship between emotion and movement offers valuable insights into human behavior, including opportunities to enhance performance, and holds promise for improving mental and physical health. This review integrates findings from multiple scientific approaches, including anatomical tracing, circuit-based dissection, and behavioral studies, conducted in both animal and human subjects. By incorporating these different methodologies, we aim to present a comprehensive overview of the current understanding of the emotional modulation of movement in both physiological and pathological conditions.

Stroke-Related Visceral Alterations: A Voxel-Based Neuroanatomic Localization Study

OBJECTIVE

Functional and morphologic changes in extracranial organs can occur after acute brain injury. The neuroanatomic correlates of such changes are not fully known. Herein, we tested the hypothesis that brain infarcts are associated with cardiac and systemic abnormalities (CSAs) in a regionally specific manner.

METHODS

We generated voxelwise p value maps of brain infarcts for poststroke plasma cardiac troponin T (cTnT) elevation, QTc prolongation, in-hospital infection, and acute stress hyperglycemia (ASH) in 1,208 acute ischemic stroke patients prospectively recruited into the Heart-Brain Interactions Study. We examined the relationship between infarct location and CSAs using a permutation-based approach and identified clusters of contiguous voxels associated with p < 0.05.

RESULTS

cTnT elevation not attributable to a known cardiac reason was detected in 5.5%, QTc prolongation in the absence of a known provoker in 21.2%, ASH in 33.9%, and poststroke infection in 13.6%. We identified significant, spatially segregated voxel clusters for each CSA. The clusters for troponin elevation and QTc prolongation mapped to the right hemisphere. There were 3 clusters for ASH, the largest of which was in the left hemisphere. We found 2 clusters for poststroke infection, one associated with pneumonia in the left and one with urinary tract infection in the right hemisphere. The relationship between infarct location and CSAs persisted after adjusting for infarct volume.

INTERPRETATION

Our results show that there are discrete regions of brain infarcts associated with CSAs. This information could be used to bootstrap toward new markers for better differentiation between neurogenic and non-neurogenic mechanisms of poststroke CSAs. ANN NEUROL 2023;94:1155-1163.

Neuroanatomy of post-stroke depression: the association between symptom clusters and lesion location

Post-stroke depression affects about 30% of stroke patients and often hampers functional recovery. The diagnosis of depression encompasses heterogeneous symptoms at emotional, motivational, cognitive, behavioural or somatic levels. Evidence indicates that depression is caused by disruption of bio-aminergic fibre tracts between prefrontal and limbic or striatal brain regions comprising different functional networks. Voxel-based lesion-symptom mapping studies reported discrepant findings regarding the association between infarct locations and depression. Inconsistencies may be due to the usage of sum scores, thereby mixing different symptoms of depression. In this cross-sectional study, we used multivariate support vector regression for lesion-symptom mapping to identify regions significantly involved in distinct depressive symptom domains and global depression. MRI lesion data were included from 200 patients with acute first-ever ischaemic stroke (mean 0.9 ± 1.5 days of post-stroke). The Montgomery-Åsberg Depression Rating interview assessed depression severity in five symptom domains encompassing motivational, emotional and cognitive symptoms deficits, anxiety and somatic symptoms and was examined 8.4 days of post-stroke (±4.3). We found that global depression severity, irrespective of individual symptom domains, was primarily linked to right hemispheric lesions in the dorsolateral prefrontal cortex and inferior frontal gyrus. In contrast, when considering distinct symptom domains individually, the analyses yielded much more sensitive results in regions where the correlations with the global depression score yielded no effects. Accordingly, motivational deficits were associated with lesions in orbitofrontal cortex, dorsolateral prefrontal cortex, pre- and post-central gyri and basal ganglia, including putamen and pallidum. Lesions affecting the dorsal thalamus, anterior insula and somatosensory cortex were significantly associated with emotional symptoms such as sadness. Damage to the dorsolateral prefrontal cortex was associated with concentration deficits, cognitive symptoms of guilt and self-reproach. Furthermore, somatic symptoms, including loss of appetite and sleep disturbances, were linked to the insula, parietal operculum and amygdala lesions. Likewise, anxiety was associated with lesions impacting the central operculum, insula and inferior frontal gyrus. Interestingly, symptoms of anxiety were exclusively left hemispheric, whereas the lesion-symptom associations of the other domains were lateralized to the right hemisphere. In conclusion, this large-scale study shows that in acute stroke patients, differential post-stroke depression symptom domains are associated with specific structural correlates. Our findings extend existing concepts on the neural underpinnings of depressive symptoms, indicating that differential lesion patterns lead to distinct depressive symptoms in the first weeks of post-stroke. These findings may facilitate the development of personalized treatments to improve post-stroke rehabilitation.

The transitive inference task to study the neuronal correlates of memory-driven decision making: A monkey neurophysiology perspective

A vast amount of literature agrees that rank-ordered information as A>B>C>D>E>F is mentally represented in spatially organized schemas after learning. This organization significantly influences the process of decision-making, using the acquired premises, i.e. deciding if B is higher than D is equivalent to comparing their position in this space. The implementation of non-verbal versions of the transitive inference task has provided the basis for ascertaining that different animal species explore a mental space when deciding among hierarchically organized memories. In the present work, we reviewed several studies of transitive inference that highlighted this ability in animals and, consequently, the animal models developed to study the underlying cognitive processes and the main neural structures supporting this ability. Further, we present the literature investigating which are the underlying neuronal mechanisms. Then we discuss how non-human primates represent an excellent model for future studies, providing ideal resources for better understanding the neuronal correlates of decision-making through transitive inference tasks.

Mapping Lesion-Related Epilepsy to a Human Brain Network

Importance

It remains unclear why lesions in some locations cause epilepsy while others do not. Identifying the brain regions or networks associated with epilepsy by mapping these lesions could inform prognosis and guide interventions.

Objective

To assess whether lesion locations associated with epilepsy map to specific brain regions and networks.

Design, Setting, and Participants

This case-control study used lesion location and lesion network mapping to identify the brain regions and networks associated with epilepsy in a discovery data set of patients with poststroke epilepsy and control patients with stroke. Patients with stroke lesions and epilepsy (n = 76) or no epilepsy (n = 625) were included. Generalizability to other lesion types was assessed using 4 independent cohorts as validation data sets. The total numbers of patients across all datasets (both discovery and validation datasets) were 347 with epilepsy and 1126 without. Therapeutic relevance was assessed using deep brain stimulation sites that improve seizure control. Data were analyzed from September 2018 through December 2022. All shared patient data were analyzed and included; no patients were excluded.

Main Outcomes and Measures

Epilepsy or no epilepsy.

Results

Lesion locations from 76 patients with poststroke epilepsy (39 [51%] male; mean [SD] age, 61.0 [14.6] years; mean [SD] follow-up, 6.7 [2.0] years) and 625 control patients with stroke (366 [59%] male; mean [SD] age, 62.0 [14.1] years; follow-up range, 3-12 months) were included in the discovery data set. Lesions associated with epilepsy occurred in multiple heterogenous locations spanning different lobes and vascular territories. However, these same lesion locations were part of a specific brain network defined by functional connectivity to the basal ganglia and cerebellum. Findings were validated in 4 independent cohorts including 772 patients with brain lesions (271 [35%] with epilepsy; 515 [67%] male; median [IQR] age, 60 [50-70] years; follow-up range, 3-35 years). Lesion connectivity to this brain network was associated with increased risk of epilepsy after stroke (odds ratio [OR], 2.82; 95% CI, 2.02-4.10; P < .001) and across different lesion types (OR, 2.85; 95% CI, 2.23-3.69; P < .001). Deep brain stimulation site connectivity to this same network was associated with improved seizure control (r, 0.63; P < .001) in 30 patients with drug-resistant epilepsy (21 [70%] male; median [IQR] age, 39 [32-46] years; median [IQR] follow-up, 24 [16-30] months).

Conclusions and Relevance

The findings in this study indicate that lesion-related epilepsy mapped to a human brain network, which could help identify patients at risk of epilepsy after a brain lesion and guide brain stimulation therapies.

Reliability and validity of DTI-based indirect disconnection measures

White matter connections enable the interaction within and between brain networks. Brain lesions can cause structural disconnections that disrupt networks and thereby cognitive functions supported by them. In recent years, novel methods have been developed to quantify the extent of structural disconnection after focal lesions, using tractography data from healthy controls. These methods, however, are indirect and their reliability and validity have yet to be fully established. In this study, we present our implementation of this approach, in a tool supplemented by uncertainty metrics for the predictions overall and at voxel-level. These metrics give an indication of the reliability and are used to compare predictions with direct measures from patients' diffusion tensor imaging (DTI) data in a sample of 95 first-ever stroke patients. Results show that, except for small lesions, the tool can predict fiber loss with high reliability and compares well to direct patient DTI estimates. Clinical utility of the method was demonstrated using lesion data from a subset of patients suffering from hemianopia. Both tract-based measures outperformed lesion localization in mapping visual field defects and showed a network consistent with the known anatomy of the visual system. This study offers an important contribution to the validation of structural disconnection mapping. We show that indirect measures of structural disconnection can be a reliable and valid substitute for direct estimations of fiber loss after focal lesions. Moreover, based on these results, we argue that indirect structural disconnection measures may even be preferable to lower-quality single subject diffusion MRI when based on high-quality healthy control datasets.

Associations of lesion location, structural disconnection, and functional diaschisis with depressive symptoms post stroke

Introduction

Post-stroke depressive symptoms (PSDS) are common and relevant for patient outcome, but their complex pathophysiology is ill understood. It likely involves social, psychological and biological factors. Lesion location is a readily available information in stroke patients, but it is unclear if the neurobiological substrates of PSDS are spatially localized. Building on previous analyses, we sought to determine if PSDS are associated with specific lesion locations, structural disconnection and/or localized functional diaschisis.

Methods

In a prospective observational study, we examined 270 patients with first-ever stroke with the Hospital Anxiety and Depression Scale (HADS) around 6 months post-stroke. Based on individual lesion locations and the depression subscale of the HADS we performed support vector regression lesion-symptom mapping, structural-disconnection-symptom mapping and functional lesion network-symptom-mapping, in a reanalysis of this previously published cohort to infer structure-function relationships.

Results

We found that depressive symptoms were associated with (i) lesions in the right insula, right putamen, inferior frontal gyrus and right amygdala and (ii) structural disconnection in the right temporal lobe. In contrast, we found no association with localized functional diaschisis. In addition, we were unable to confirm a previously described association between depressive symptom load and a network damage score derived from functional disconnection maps.

Discussion

Based on our results, and other recent lesion studies, we see growing evidence for a prominent role of right frontostriatal brain circuits in PSDS.

Disentangling poststroke cognitive deficits and their neuroanatomical correlates through combined multivariable and multioutcome lesion-symptom mapping

Studies in patients with brain lesions play a fundamental role in unraveling the brain's functional anatomy. Lesion-symptom mapping (LSM) techniques can relate lesion location to cognitive performance. However, a limitation of current LSM approaches is that they can only evaluate one cognitive outcome at a time, without considering interdependencies between different cognitive tests. To overcome this challenge, we implemented canonical correlation analysis (CCA) as combined multivariable and multioutcome LSM approach. We performed a proof-of-concept study on 1075 patients with acute ischemic stroke to explore whether addition of CCA to a multivariable single-outcome LSM approach (support vector regression) could identify infarct locations associated with deficits in three well-defined verbal memory functions (encoding, consolidation, retrieval) based on four verbal memory subscores derived from the Seoul Verbal Learning Test (immediate recall, delayed recall, recognition, learning ability). We evaluated whether CCA could extract cognitive score patterns that matched prior knowledge of these verbal memory functions, and if these patterns could be linked to more specific infarct locations than through single-outcome LSM alone. Two of the canonical modes identified with CCA showed distinct cognitive patterns that matched prior knowledge on encoding and consolidation. In addition, CCA revealed that each canonical mode was linked to a distinct infarct pattern, while with multivariable single-outcome LSM individual verbal memory subscores were associated with largely overlapping patterns. In conclusion, our findings demonstrate that CCA can complement single-outcome LSM techniques to help disentangle cognitive functions and their neuroanatomical correlates.

Brain lesion locations associated with secondary seizure generalization in tumors and strokes

Structural brain lesions are the most common cause of adult-onset epilepsy. The lesion location may contribute to the risk for epileptogenesis, but whether specific lesion locations are associated with a risk for secondary seizure generalization from focal to bilateral tonic-clonic seizures, is unknown. We identified patients with a diagnosis of adult-onset epilepsy caused by an ischemic stroke or a tumor diagnosed at the Turku University Hospital in 2004-2017. Lesion locations were segmented on patient-specific MR imaging and transformed to a common brain atlas (MNI space). Both region-of-interest analyses (intersection with the cortex, hemisphere, and lobes) and voxel-wise analyses were conducted to identify the lesion locations associated with focal to bilateral tonic-clonic compared to focal seizures. We included 170 patients with lesion-induced epilepsy (94 tumors, 76 strokes). Lesions predominantly localized in the cerebral cortex (OR 2.50, 95% C.I. 1.21-5.15, p = .01) and right hemisphere (OR 2.22, 95% C.I. 1.17-4.20, p = .01) were independently associated with focal to bilateral tonic-clonic seizures. At the lobar-level, focal to bilateral tonic-clonic seizures were associated with lesions in the right frontal cortex (OR 4.41, 95% C.I. 1.44-13.5, p = .009). No single voxels were significantly associated with seizure type. These effects were independent of lesion etiology. Our results demonstrate that lesion location is associated with the risk for secondary generalization of epileptic seizures. These findings may contribute to identifying patients at risk for focal to bilateral tonic-clonic seizures.

Bayesian lesion-deficit inference with Bayes factor mapping: Key advantages, limitations, and a toolbox

Statistical lesion-symptom mapping is largely dominated by frequentist approaches with null hypothesis significance testing. They are popular for mapping functional brain anatomy but are accompanied by some challenges and limitations. The typical analysis design and the structure of clinical lesion data are linked to the multiple comparison problem, an association problem, limitations to statistical power, and a lack of insights into evidence for the null hypothesis. Bayesian lesion deficit inference (BLDI) could be an improvement as it collects evidence for the null hypothesis, i.e. the absence of effects, and does not accumulate α-errors with repeated testing. We implemented BLDI by Bayes factor mapping with Bayesian t-tests and general linear models and evaluated its performance in comparison to frequentist lesion-symptom mapping with a permutation-based family-wise error correction. We mapped the voxel-wise neural correlates of simulated deficits in an in-silico-study with 300 stroke patients, and the voxel-wise and disconnection-wise neural correlates of phonemic verbal fluency and constructive ability in 137 stroke patients. Both the performance of frequentist and Bayesian lesion-deficit inference varied largely across analyses. In general, BLDI could find areas with evidence for the null hypothesis and was statistically more liberal in providing evidence for the alternative hypothesis, i.e. the identification of lesion-deficit associations. BLDI performed better in situations in which the frequentist method is typically strongly limited, for example with on average small lesions and in situations with low power, where BLDI also provided unprecedented transparency in terms of the informative value of the data. On the other hand, BLDI suffered more from the association problem, which led to a pronounced overshoot of lesion-deficit associations in analyses with high statistical power. We further implemented a new approach to lesion size control, adaptive lesion size control, that, in many situations, was able to counter the limitations imposed by the association problem, and increased true evidence both for the null and the alternative hypothesis. In summary, our results suggest that BLDI is a valuable addition to the method portfolio of lesion-deficit inference with some specific and exclusive advantages: it deals better with smaller lesions and low statistical power (i.e. small samples and effect sizes) and identifies regions with absent lesion-deficit associations. However, it is not superior to established frequentist approaches in all respects and therefore not to be seen as a general replacement. To make Bayesian lesion-deficit inference widely accessible, we published an R toolkit for the analysis of voxel-wise and disconnection-wise data.

Enhancing the resolution of behavioral measures: Key observations during a forty year career in behavioral neuroscience

This manuscript reviews several key observations from the research program of Professor John P. Bruno that are believed to have significantly advanced our understanding of the brain's mediation of behavior. This review focuses on findings within several important research areas in behavioral neuroscience, including a) age-dependent neurobehavioral plasticity following brain damage; b) the role of the cortical cholinergic system in attentional processing and cognitive flexibility; and c) the design and validation of animal models of cognitive deficits in schizophrenia. In selecting these observations, emphasis was given to examples in which the heuristic potency was increased by maximizing the resolution and microanalysis of behavioral assays in the same fashion as one typically refines neuronal manipulations. Professor Bruno served the International Behavioral Neuroscience Society (IBNS) as an IBNS Fellow (1995-present) and President of the IBNS (2001-02).

"The great mixing machine": multisensory integration and brain-breath coupling in the cerebral cortex

It is common to distinguish between "holist" and "reductionist" views of brain function, where the former envisions the brain as functioning as an indivisible unit and the latter as a collection of distinct units that serve different functions. Opposing reductionism, a number of researchers have pointed out that cortical network architecture does not respect functional boundaries, and the neuroanatomist V. Braitenberg proposed to understand the cerebral cortex as a "great mixing machine" of neuronal activity from sensory inputs, motor commands, and intrinsically generated processes. In this paper, we offer a contextualization of Braitenberg's point, and we review evidence for the interactions of neuronal activity from multiple sensory inputs and intrinsic neuronal processes in the cerebral cortex. We focus on new insights from studies on audiovisual interactions and on the influence of respiration on brain functions, which do not seem to align well with "reductionist" views of areal functional boundaries. Instead, they indicate that functional boundaries are fuzzy and context dependent. In addition, we discuss the relevance of the influence of sensory, proprioceptive, and interoceptive signals on cortical activity for understanding brain-body interactions, highlight some of the consequences of these new insights for debates on embodied cognition, and offer some suggestions for future studies.

The impact of etiology in lesion-symptom mapping - A direct comparison between tumor and stroke

INTRODUCTION

Lesion-symptom mapping is a key tool in understanding the relationship between brain structures and behavior. However, the behavioral consequences of lesions from different etiologies may vary because of how they affect brain tissue and how they are distributed. The inclusion of different etiologies would increase the statistical power but has been critically debated. Meanwhile, findings from lesion studies are a valuable resource for clinicians and used across different etiologies. Therefore, the main objective of the present study was to directly compare lesion-symptom maps for memory and language functions from two populations, a tumor versus a stroke population.

METHODS

Data from two different studies were combined. Both the brain tumor (N = 196) and stroke (N = 147) patient populations underwent neuropsychological testing and an MRI, pre-operatively for the tumor population and within three months after stroke. For this study, we selected two internationally widely used standardized cognitive tasks, the Rey Auditory Verbal Learning Test and the Verbal Fluency Test. We used a state-of-the-art machine learning-based, multivariate voxel-wise approach to produce lesion-symptom maps for these cognitive tasks for both populations separately and combined.

RESULTS

Our lesion-symptom mapping results for the separate patient populations largely followed the expected neuroanatomical pattern based on previous literature. Substantial differences in lesion distribution hindered direct comparison. Still, in brain areas with adequate coverage in both groups, considerable LSM differences between the two populations were present for both memory and fluency tasks. Post-hoc analyses of these locations confirmed that the cognitive consequences of focal brain damage varied between etiologies.

CONCLUSION

The differences in the lesion-symptom maps between the stroke and tumor population could partly be explained by differences in lesion volume and topography. Despite these methodological limitations, both the lesion-symptom mapping results and the post-hoc analyses confirmed that etiology matters when investigating the cognitive consequences of lesions with lesion-symptom mapping. Therefore, caution is advised with generalizing lesion-symptom results across etiologies.

Disconnectomics to unravel the network underlying deficits of spatial exploration and attention

Spatial attention and exploration are related to a predominantly right hemispheric network structure. However, the areas of the brain involved and their exact role is still debated. Spatial neglect following right hemispheric stroke lesions has been frequently viewed as a model to study these processes in humans. Previous investigations on the anatomical basis on spatial neglect predominantly focused on focal brain damage and lesion-behaviour mapping analyses. This approach might not be suited to detect remote areas structurally spared but which might contribute to the behavioural deficit. In the present study of a sample of 203 right hemispheric stroke patients, we combined connectome lesion-symptom mapping with multivariate support vector regression to unravel the complex and disconnected network structure in spatial neglect. We delineated three central nodes that were extensively disconnected from other intrahemispheric areas, namely the right superior parietal lobule, the insula, and the temporal pole. Additionally, the analysis allocated central roles within this network to the inferior frontal gyrus (pars triangularis and opercularis), right middle temporal gyrus, right temporal pole and left and right orbitofrontal cortices, including interhemispheric disconnection. Our results suggest that these structures-although not necessarily directly damaged-might play a role within the network underlying spatial neglect in humans.

Neurocognition in adults with intracranial tumors: does location really matter?

OBJECTIVE

As preservation of cognitive functioning increasingly becomes important in the light of ameliorated survival after intracranial tumor treatments, identification of eloquent brain areas would enable optimization of these treatments.

METHODS

This cohort study enrolled adult intracranial tumor patients who received neuropsychological assessments pre-irradiation, estimating processing speed, verbal fluency and memory. Anatomical magnetic resonance imaging scans were used for multivariate voxel-wise lesion-symptom predictions of the test scores (corrected for age, gender, educational level, histological subtype, surgery, and tumor volume). Potential effects of histological and molecular subtype and corresponding WHO grades on the risk of cognitive impairment were investigated using Chi square tests. P-values were adjusted for multiple comparisons (p < .001 and p < .05 for voxel- and cluster-level, resp.).

RESULTS

A cohort of 179 intracranial tumor patients was included [aged 19-85 years, median age (SD) = 58.46 (14.62), 50% females]. In this cohort, test-specific impairment was detected in 20-30% of patients. Higher WHO grade was associated with lower processing speed, cognitive flexibility and delayed memory in gliomas, while no acute surgery-effects were found. No grading, nor surgery effects were found in meningiomas. The voxel-wise analyses showed that tumor locations in left temporal areas and right temporo-parietal areas were related to verbal memory and processing speed, respectively.

INTERPRETATION

Patients with intracranial tumors affecting the left temporal areas and right temporo-parietal areas might specifically be vulnerable for lower verbal memory and processing speed. These specific patients at-risk might benefit from early-stage interventions. Furthermore, based on future validation studies, imaging-informed surgical and radiotherapy planning could further be improved.

Mapping pontocerebellar connectivity with diffusion MRI

The cerebellum's involvement in cognitive, affective and motor functions is mediated by connections to different regions of the cerebral cortex. A distinctive feature of cortico-cerebellar loops that has been demonstrated in the animal work is a topographic organization that is preserved across its corticopontine, pontocerebellar, and cerebello-thalmo-cortical segments. Here we used tractography derived from diffusion imaging data to characterize the connections between the pons and the individual lobules of the cerebellum and generate a parcellation of the pons and middle cerebellar peduncle based on the pattern of connectivity. We identified a rostral to caudal gradient in the pons, similar to that observed in the animal work, such that rostral regions were preferentially connected to cerebellar lobules involved in non-motor, and caudal regions with motor regions. These findings advance our fundamental understanding of the cerebellum, and the parcellations we generated provide context for future research into the pontocerebellar tract's involvement in health and disease.

Mapping correlated neurological deficits after stroke to distributed brain networks

Understanding the relationships between brain organization and behavior is a central goal of neuroscience. Traditional teaching emphasizes that the human cerebrum includes many distinct areas for which damage or dysfunction would lead to a unique and specific behavioral syndrome. This teaching implies that brain areas correspond to encapsulated modules that are specialized for specific cognitive operations. However, empirically, local damage from stroke more often produces one of a small number of clusters of deficits and disrupts brain-wide connectivity in a small number of predictable ways (relative to the vast complexity of behavior and brain connectivity). Behaviors that involve shared operations show correlated deficits following a stroke, consistent with a low-dimensional behavioral space. Because of the networked organization of the brain, local damage from a stroke can result in widespread functional abnormalities, matching the low dimensionality of behavioral deficit. In alignment with this, neurological disease, psychiatric disease, and altered brain states produce behavioral changes that are highly correlated across a range of behaviors. We discuss how known structural and functional network priors in addition to graph theoretical concepts such as modularity and entropy have provided inroads to understanding this more complex relationship between brain and behavior. This model for brain disease has important implications for normal brain-behavior relationships and the treatment of neurological and psychiatric diseases.

Putting music to trial: Consensus on key methodological challenges investigating music-based rehabilitation

Major advances in music neuroscience have fueled a growing interest in music-based neurological rehabilitation among researchers and clinicians. Musical activities are excellently suited to be adapted for clinical practice because of their multisensory nature, their demands on cognitive, language, and motor functions, and music's ability to induce emotions and regulate mood. However, the overall quality of music-based rehabilitation research remains low to moderate for most populations and outcomes. In this consensus article, expert panelists who participated in the Neuroscience and Music VII conference in June 2021 address methodological challenges relevant to music-based rehabilitation research. The article aims to provide guidance on challenges related to treatment, outcomes, research designs, and implementation in music-based rehabilitation research. The article addresses how to define music-based rehabilitation, select appropriate control interventions and outcomes, incorporate technology, and consider individual differences, among other challenges. The article highlights the value of the framework for the development and evaluation of complex interventions for music-based rehabilitation research and the need for stronger methodological rigor to allow the widespread implementation of music-based rehabilitation into regular clinical practice.

A depression network caused by brain tumours

To systematically analyse and discuss whether suppressive heterogeneous brain tumours (BTs) belong to a common brain network and provide a theoretical basis for identifying BT patients with a high risk of depression and select therapeutic targets for clinical treatment. The PubMed database was systematically searched to obtain relevant case reports, and lesion locations were manually traced to standardised brain templates according to ITK-SNAP descriptive literature. Resting-state functional magnetic resonance imaging data sets were collected from 1,000 healthy adults aged 18-35 years. Each lesion location or functional connectivity area of the lesion network. Connectivity analysis was performed in an MN152 space, and Fisher z-transformation was applied to normalise the distribution of each value in the functional connectivity correlation map, and T maps of each tumour location network were calculated with the T score of individual voxels. This T score indicates the statistical significance of voxelwise connectivity at each tumour location. The lesion networks were thresholded at T = 7, creating binarised maps of brain regions connecting tumour locations, overlaying network maps to identify tumour-sensitive hubs and also assessing specific hubs with other conditional controls. A total of 18 patients describing depression following focal BTs were included. Of these cases, it was reported that depression-related tumours were unevenly distributed in the brain: 89% (16/18) were positively correlated with the left striatum, and the peak of the left striatum lesion network continuously overlapped. The depression-related tumour location was consistent with the tumour suppressor network (89%). These results suggest that sensitive hubs are aligned with specific networks, and specific hubs are aligned with sensitive networks. Brain tumour-related depression differs from acute lesion-related depression and may be related to the mapping of tumours to depression-related brain networks. It can provide an observational basis for the neuroanatomical basis of BT-related depression and a theoretical basis for identifying patients with BTs at high risk of depression and their subsequent clinical diagnosis and treatment.

Inferring the dynamical effects of stroke lesions through whole-brain modeling

Understanding the effect of focal lesions (stroke) on brain structure-function traditionally relies on behavioral analyses and correlation with neuroimaging data. Here we use structural disconnection maps from individual lesions to derive a causal mechanistic generative whole-brain model able to explain both functional connectivity alterations and behavioral deficits induced by stroke. As compared to other models that use only the local lesion information, the similarity to the empirical fMRI connectivity increases when the widespread structural disconnection information is considered. The presented model classifies behavioral impairment severity with higher accuracy than other types of information (e.g.: functional connectivity). We assessed topological measures that characterize the functional effects of damage. With the obtained results, we were able to understand how network dynamics change emerge, in a nontrivial way, after a stroke injury of the underlying complex brain system. This type of modeling, including structural disconnection information, helps to deepen our understanding of the underlying mechanisms of stroke lesions.

Structural connectome constrained graphical lasso for MEG partial coherence

Structural connectivity provides the backbone for communication between neural populations. Since axonal transmission occurs on a millisecond time scale, measures of M/EEG functional connectivity sensitive to phase synchronization, such as coherence, are expected to reflect structural connectivity. We develop a model of MEG functional connectivity whose edges are constrained by the structural connectome. The edge strengths are defined by partial coherence, a measure of conditional dependence. We build a new method-the adaptive graphical lasso (AGL)-to fit the partial coherence to perform inference on the hypothesis that the structural connectome is reflected in MEG functional connectivity. In simulations, we demonstrate that the structural connectivity's influence on the partial coherence can be inferred using the AGL. Further, we show that fitting the partial coherence is superior to alternative methods at recovering the structural connectome, even after the source localization estimates required to map MEG from sensors to the cortex. Finally, we show how partial coherence can be used to explore how distinct parts of the structural connectome contribute to MEG functional connectivity in different frequency bands. Partial coherence offers better estimates of the strength of direct functional connections and consequently a potentially better estimate of network structure.

Activation network mapping for integration of heterogeneous fMRI findings

Functional neuroimaging techniques have been widely used to probe the neural substrates of facial emotion processing in healthy people. However, findings are largely inconsistent across studies. Here, we introduce a new technique termed activation network mapping to examine whether heterogeneous functional magnetic resonance imaging findings localize to a common network for emotion processing. First, using the existing method of activation likelihood estimation meta-analysis, we showed that individual-brain-based reproducibility was low across studies. Second, using activation network mapping, we found that network-based reproducibility across these same studies was higher. Validation analysis indicated that the activation network mapping-localized network aligned with stimulation sites, structural abnormalities and brain lesions that disrupt facial emotion processing. Finally, we verified the generality of the activation network mapping technique by applying it to another cognitive process, that is, rumination. Activation network mapping may potentially be broadly applicable to localize brain networks of cognitive functions.

A precise language network revealed by the independent component-based lesion mapping in post-stroke aphasia

Brain lesion mapping studies have provided the strongest evidence regarding the neural basis of cognition. However, it remained a problem to identify symptom-specific brain networks accounting for observed clinical and neuroanatomical heterogeneity. Independent component analysis (ICA) is a statistical method that decomposes mixed signals into multiple independent components. We aimed to solve this issue by proposing an independent component-based lesion mapping (ICLM) method to identify the language network in patients with moderate to severe post-stroke aphasia. Lesions were first extracted from 49 patients with post-stroke aphasia as masks applied to fMRI data in a cohort of healthy participants to calculate the functional connectivity (FC) within the masks and non-mask brain voxels. ICA was further performed on a reformatted FC matrix to extract multiple independent networks. Specifically, we found that one of the lesion-related independent components (ICs) highly resembled classical language networks. Moreover, the damaged level within the language-related lesioned network is strongly associated with language deficits, including aphasia quotient, naming, and auditory comprehension scores. In comparison, none of the other two traditional lesion mapping methods found any regions responsible for language dysfunction. The language-related lesioned network extracted with the ICLM method showed high specificity in detecting aphasia symptoms compared with the performance of resting ICs and classical language networks. In total, we detected a precise language network in patients with aphasia and proved its efficiency in the relationship with language symptoms. In general, our ICLM could successfully identify multiple lesion-related networks from complicated brain diseases, and be used as an effective tool to study brain-behavior relationships and provide potential biomarkers of particular clinical behavioral deficits.

Lesions causing central sleep apnea localize to one common brain network

Objectives

To characterize the specific brain regions for central sleep apnea (CSA) and identify its functional connectivity network.

Methods

We performed a literature search and identified 27 brain injuries causing CSA. We used a recently validated methodology termed “lesion network mapping” to identify the functional brain network subtending the pathophysiology of CSA. Two separate statistical approaches, the two-sample t-test and the Liebermeister test, were used to evaluate the specificity of this network for CSA through a comparison of our results with those of two other neurological syndromes. An additional independent cohort of six CSA cases was used to assess reproducibility.

Results

Our results showed that, despite lesions causing CSA being heterogeneous for brain localization, they share a common brain network defined by connectivity to the middle cingulate gyrus and bilateral cerebellar posterior lobes. This CSA-associated connectivity pattern was unique when compared with lesions causing the other two neurological syndromes. The CAS-specific regions were replicated by the additional independent cohort of six CSA cases. Finally, we found that all lesions causing CSA aligned well with the network defined by connectivity to the cingulate gyrus and bilateral cerebellar posterior lobes.

Conclusion

Our results suggest that brain injuries responsible for CSA are part of a common brain network defined by connectivity to the middle cingulate gyrus and bilateral cerebellar posterior lobes, lending insight into the neuroanatomical substrate of CSA.

A comparison of uni- and multi-variate methods for identifying brain networks activated by cognitive tasks using intracranial EEG

Cognitive tasks are commonly used to identify brain networks involved in the underlying cognitive process. However, inferring the brain networks from intracranial EEG data presents several challenges related to the sparse spatial sampling of the brain and the high variability of the EEG trace due to concurrent brain processes. In this manuscript, we use a well-known facial emotion recognition task to compare three different ways of analyzing the contrasts between task conditions: permutation cluster tests, machine learning (ML) classifiers, and a searchlight implementation of multivariate pattern analysis (MVPA) for intracranial sparse data recorded from 13 patients undergoing presurgical evaluation for drug-resistant epilepsy. Using all three methods, we aim at highlighting the brain structures with significant contrast between conditions. In the absence of ground truth, we use the scientific literature to validate our results. The comparison of the three methods’ results shows moderate agreement, measured by the Jaccard coefficient, between the permutation cluster tests and the machine learning [0.33 and 0.52 for the left (LH) and right (RH) hemispheres], and 0.44 and 0.37 for the LH and RH between the permutation cluster tests and MVPA. The agreement between ML and MVPA is higher: 0.65 for the LH and 0.62 for the RH. To put these results in context, we performed a brief review of the literature and we discuss how each brain structure’s involvement in the facial emotion recognition task.

Discussion on "distributional independent component analysis for diverse neuroimaging modalities" by Ben Wu, Subhadip Pal, Jian Kang, and Ying Guo

Wu et al. have made an important contribution to the methodology for data-driven analysis of MRI data. However, we wish to challenge the authors on new potential applications of their approach beyond diffusion tensor imaging data, and to think carefully about the impact of random initialization implicit in their method. We illustrate the variability found from re-analyzing the supplied demonstration data multiple times, finding that the discovered independent components have a wide range of reliability, from nearly perfect overlap to no overlap at all.

Indirect structural disconnection-symptom mapping

In vivo tracking of white matter fibres catalysed a modern perspective on the pivotal role of brain connectome disruption in neuropsychological deficits. However, the examination of white matter integrity in neurological patients by diffusion-weighted magnetic resonance imaging bears conceptual limitations and is not widely applicable, as it requires imaging-compatible patients and resources beyond the capabilities of many researchers. The indirect estimation of structural disconnection offers an elegant and economical alternative. For this approach, a patient's structural lesion information and normative connectome data are combined to estimate different measures of lesion-induced structural disconnection. Using one of several toolboxes, this method is relatively easy to implement and is even available to scientists without expertise in fibre tracking analyses. Nevertheless, the anatomo-behavioural statistical mapping of structural brain disconnection requires analysis steps that are not covered by these toolboxes. In this paper, we first review the current state of indirect lesion disconnection estimation, the different existing measures, and the available software. Second, we aim to fill the remaining methodological gap in statistical disconnection-symptom mapping by providing an overview and guide to disconnection data and the statistical mapping of their relationship to behavioural measurements using either univariate or multivariate statistical modelling. To assist in the practical implementation of statistical analyses, we have included software tutorials and analysis scripts.

fMRI informed voxel-based lesion analysis to identify lesions associated with right-hemispheric activation in aphasia recovery

Several mechanisms have been attributed to post-stroke loss and recovery of language functions. However, the significance and timing of domain-general and homotopic right-hemispheric activation is controversial. We aimed to examine the effect of left-hemispheric lesion location and time post-stroke on right-hemispheric activation. Voxel-based lesion analyses were informed by auditory language-related fMRI activation of 71 patients with left middle cerebral artery stroke examined longitudinally in the acute, subacute and early chronic phase. Language activation was determined in several right-hemispheric regions of interest and served as regressor of interest for voxel-based lesion analyses. We found that an acute to chronic increase of language activation in the right supplementary motor area was associated with lesions to the left extreme capsule as part of the ventral language pathway. Importantly, this activation increase correlated significantly with improvement of out-of-scanner comprehension abilities. We interpret our findings in terms of successful domain-general compensation in patients with critical left frontotemporal disconnection due to damage to the ventral language pathway but relatively spared cortical language areas.