Dorsal Attention Research Articles

Spatiotemporal integration of contextual and sensory information within the cortical hierarchy in human pain experience.

Pain is not a mere reflection of noxious input. Rather, it is constructed through the dynamic integration of current predictions with incoming sensory input. However, the temporal dynamics of the behavioral and neural processes underpinning this integration remain elusive. In the current study involving 59 human participants, we identified a series of brain mediators that integrated cue-induced expectations with noxious inputs into ongoing pain predictions using a semicircular scale designed to capture rating trajectories. Temporal mediation analysis revealed that during the early-to-mid stages of integration, the frontoparietal and dorsal attention network regions, such as the lateral prefrontal, premotor, and parietal cortex, mediated the cue effects. Conversely, during the mid-to-late stages of integration, the somatomotor network regions mediated the effects of stimulus intensity, suggesting that the integration occurs along the cortical hierarchy from the association to sensorimotor brain systems. Our findings advance the understanding of how the brain integrates contextual and sensory information into pain experience over time.

CNSC-16. ASSOCIATION BETWEEN RESTING-STATE-NETWORK FUNCTIONAL CONNECTIVITY AND SURVIVAL IN RECURRENT GLIOMAS DIAGNOSED USING FET PET

Abstract BACKGROUND Functional connectivity between glioma-infiltrated brain tissue and remote cortical regions has been linked to overall survival (OS) in patients with newly diagnosed glioblastoma. We studied the association of functional connectivity between tumor and resting-state networks and OS in patients with recurrent gliomas. PATIENTS AND METHODS The study included 82 patients (26-81 years; median ECOG performance score, 0) with recurrent gliomas (grade 4 glioblastoma, n=57; grade 3 and 4 astrocytoma, n=12; grade 2 and 3 oligodendroglioma, n=13) who had undergone first-line therapy. Diagnosis of recurrence was confirmed by amino acid PET using the tracer O-(2-[18F]-fluoroethyl)-L-tyrosine (FET). FET PET and resting-state functional MRI were simultaneously acquired on a 3T hybrid PET/MR scanner. We performed a seed-based dual regression analysis to determine the functional connectivity between metabolically active tumors and seven canonical resting state networks. RESULTS Average functional connectivity was highest in oligodendrogliomas (z-score, 6.9±1.9) and was inversely related to the tumor grade (p=0.031). Univariate Cox regression analysis revealed a significant (p<0.05) positive association between OS and tumor connectivity with the visual, somatomotor, and dorsal attention networks. Subsequent stepwise multivariate Cox regression analysis refined this association to the dorsal attention network alone (HR, 0.88; 95% CI, 0.80-0.97; p=0.007). Patients with the highest functional connectivity (z-score>8.3, n=21) had a median OS of 31.4 months, while those with the lowest functional connectivity (z-score<4.3, n=21) had 10.6 months. Functional connectivity between tumors and the dorsal attention network showed an independent association with OS (HR, 0.90; 95% CI, 0.81-0.99; p=0.033) in a multivariate model, including various prognostic factors. CONCLUSION Recurrent gliomas exhibit significant functional connectivity to most of the major known resting-state networks, highest in oligodendrogliomas. The data suggest that high functional connectivity between the tumor and the dorsal attention network is an independent prognostic factor for improved OS.

NCOG-15. CONNECTOME-BASED APPROACHES FOR PRESERVING COGNITIVE FUNCTION AND MINIMIZING DELIRIUM DURING GLIOMA RESECTION: A PILOT STUDY

Abstract AIM The surgical treatment of gliomas poses significant challenges due to the involvement of complex neural networks critical for cognitive functions. This study reviews the application of connectome-based approaches to minimize cognitive impairments and delirium during glioma resection, focusing on networks related to cognitive functions: the Default Mode Network (DMN), Dorsal Attention Network (DAN), Salience Network (SN), and Central Executive Network (CEN). METHODS Ten patients underwent surgery utilizing cloud-based connectomics software (Quicktome®), which maps the brain’s structural and functional connectivity. Preoperative MRIs with structural and functional connectome were performed for preoperative planning and intraoperative navigation. Preoperative neuropsychological assessments were conducted for elective cases. Only one patient was able to undergo awake craniotomy due to various medical, neurological and psychological limitations. Intraoperatively, with connectome-navigation guidance, a connectomics-based approach was conducted to preserve the networks. A semiquantitative analysis was performed as described (https://pubmed.ncbi.nlm.nih.gov/36719613). Pre- and postoperative (inpatient and 2 week follow up) RASS and NuDESC delirium screenings were performed, with long-term cognitive evaluations and repeat connectomics imaging planned. RESULTS Three out of ten patients presented with rapid functional deterioration and needed urgent surgical decompression had postoperative delirium. Connectome analysis showed involvement of cognition-related networks in these patients. Two out of 3 patients showed improvement in delirium scores at a two-week follow-up after tumor resection. CONCLUSION This pilot study suggests that factors such as acute presentation, emergency surgery, and tumor location involving and disrupting the DMN, SN, DAN, and CEN networks may contribute to early postoperative delirium. The study may pave the way for the efficacy of connectomics in preserving cognitive functions post-operatively, particularly when awake craniotomy is not possible. This approach holds promise for improving surgical outcomes, survival, and the quality of life for patients with gliomas by mitigating the cognitive impacts of tumor resection and postoperative complications.

Assessing the causal role of the structural connectome in temporomandibular disorders: A Mendelian randomization study

ABSTRACT Objective We examined the relationships between the structural connectome and temporomandibular disorders (TMDs). Methods Bidirectional Mendelian randomization analyses were conducted using Genome-wide association studies data on the structural connectome and TMDs. Results Positive associations with TMD risk were found for white matter structural connectivity from the left hemisphere limbic network to putamen, left hemisphere salience_ventral attention network to caudate, right hemisphere visual network to thalamus, and right hemisphere salience_ventral attention network to right hemisphere control network, while negative associations were observed for connectivity from the left hemisphere control and somatomotor networks to pallidum, left hemisphere somatomotor network to right hemisphere dorsal attention network, and right hemisphere somatomotor network to hippocampus (p< 0.05). In TMD patients, connectivity from the Left-hemisphere visual network to putamen was reduced, whereas connectivity from the Left-hemisphere limbic network to left-hemisphere control network was increased (p< 0.05). Conclusion Our findings provide insights into the TMD pathogenesis.

Changes in the functional connections of the brain in patients with hypersomnia in acute ischemic stroke

BACKGROUND: The Disorders of cerebral circulation and sleep are inextricably related, since sleep disorders, including hypersomnia, are closely intertwined with cardiovascular diseases and increase the risk of stroke. Research works on visualization of functional brain changes in patients with sleep disorders and acute ischemic stroke are very few and need more study. AIM: to determine the functional connections of the brain in hypersomnia in patients with acute ischemic stroke by performing functional magnetic resonance imaging at rest. A study of 40 patients with acute ischemic stroke and sleep disorders was performed on the basis of the Federal State Budgetary Institution Almazov Research Center of the Ministry of Health of the Russian Federation. All patients underwent neurological examination, assessment of sleepiness, worry and depression, magnetic resonance imaging was performed on tomographs with a magnetic field induction force of 1.5T, using a standard protocol and special pulse sequences of T-gradient echo 3D MPRAGE and BOLD. Functional magnetic resonance imaging of the brain at rest was used to assess functional connections. Postprocessing was performed using specialized CONN-TOOLBOX software with an appropriate graphical representation of quantitative results based on the selection of areas of interest. RESULTS: Among the examined patients, 23 had hypersomnolence; of these, 8 patients were diagnosed with secondary hypersomnia associated with sleep apnea syndrome. Thus, in the examined sample, post-stroke hypersomnolence was detected in 15 patients (34%). Patients with an unspecified subtype of stroke and right-sided lesions showed a high degree of drowsiness (p 0.05). With the help of functional magnetic resonance imaging of the brain at rest, changes in functional connections between the main nodes of the default mode network with the left temporal pole, cerebellum, central cerebral cortex on the left, angular gyrus on the left, with the dorsal attention network on the right are determined (p 0.05). CONCLUSION: The use of complex magnetic resonance imaging, which includes structural and functional magnetic resonance imaging in patients with acute ischemic stroke and sleep disorders, allows us to identify structural changes and changes in functional connectivity and identify neuroimaging markers of this pathology. Hypersomnolence is typical for patients with an unspecified subtype of ischemic stroke and damage to the right hemisphere of the brain.

Brain connectomes in youth at risk for serious mental illness: a longitudinal perspective.

Identifying biomarkers for serious mental illnesses (SMI) has significant implications for prevention and early intervention. In the current study, changes in whole brain structural and functional connectomes were investigated in youth at transdiagnostic risk over a one-year period. Based on clinical assessments, participants were assigned to one of 5 groups: healthy controls (HC; n = 33), familial risk for serious mental illness (stage 0; n = 31), mild symptoms (stage 1a; n = 37), attenuated syndromes (stage 1b; n = 61), or discrete disorder (transition; n = 9). Constrained spherical deconvolution was used to generate whole brain tractography maps, which were then used to calculate connectivity matrices for graph theory analysis. Graph theory was also used to analyze correlations of functional magnetic resonance imaging (fMRI) signal between pairs of brain regions. Linear mixed models revealed structural and functional abnormalities in global metrics of small world lambda, and resting state networks involving the fronto-parietal, default mode, and deep grey matter networks, along with the visual and dorsal attention networks. Machine learning analysis additionally identified changes in nodal metrics of betweenness centrality in the angular gyrus and bilateral temporal gyri as potential features which can discriminate between the groups. Our findings further support the view that abnormalities in large scale networks (particularly those involving fronto-parietal, temporal, default mode,and deep grey matter networks) may underlie transdiagnostic risk for SMIs.

Neural bases of social facilitation and inhibition: how peer presence affects elementary eye movements.

Social Facilitation/Inhibition (SFI) refers to how others' presence influences task performance positively or negatively. Our previous study revealed that peer presence modulated saccadic eye movements, a fundamental sensorimotor activity. Pro- and anti-saccades were either facilitated or inhibited depending on trial block complexity (Tricoche et al., 2020). In the present fMRI study, we adapted our paradigm to investigate the neural basis of SFI on saccades. Considering inter- and intra-individual variabilities, we evaluated the shared and distinct neural patterns between social facilitation and inhibition. We predicted an involvement of the saccade-related and attention networks, alongside the Theory-of-Mind (ToM) network, with opposite activity changes between facilitation and inhibition. Results confirmed peer presence modulation in fronto-parietal areas related to saccades and attention, in opposite directions for facilitation and inhibition. Additionally, the ventral attention network was modulated during inhibition. Default mode regions, including ToM areas, were also modulated. Finally, pupil size, often linked to arousal, increased with peers and correlated with dorsal attention regions and anterior insula activities. These results suggest that SFI engages task-specific and domain-general networks, modulated differently based on observed social effect. Attention network seemed to play a central role at both basic (linked to arousal or vigilance) and cognitive control levels.

Distinct Neural Mechanisms Underlying Dual-Task Priority During Gait Across Cognitive and Motor Networks.

Background: Prioritization strategy during gait significantly influences gait performance and successful gait relies on interactions between cognitive and motor functions. This study aimed to examine the within- and between-network connectivities of cognitive and motor networks associated with dual-task priority during gait. Methods: Twenty-nine healthy individuals (66.86 ± 8.53 years) underwent the timed-up-and-go (TUG) test alone, TUG with a cognitive task, and the cognitive task alone. The cognitive task involved sequentially subtracting three from a random number between 50 and 100. The resting-state functional magnetic resonance imaging was acquired on the same day. Using independent component analysis, the dorsal attention network (DAN), frontoparietal network (FPN), primary motor network (PM), and lateral motor network were assessed. The participants were divided into cognitive and motor priority groups based on the modified attention allocation index (mAAI). Group comparisons of within- and between-network connectivity were conducted using permutation tests. Additionally, correlation analysis was employed to investigate the association between-network connectivity and task priority. Results: The cognitive priority group showed cognitive dual-task facilitation. In comparison to the motor priority group, they also showed comparable motor dual-task costs and lower combined dual-task costs. They exhibited increased within-network connectivity in the left FPN and enhanced between-network connectivity between the right FPN and both the DAN and PM. These between-network connectivities were negatively correlated with mAAI scores. Conclusion: The results suggest distinct neural mechanisms across cognitive and motor networks based on individuals' dual-task strategies. This may have implications for understanding gait performance in complex contexts.

Retinotopic coding organizes the interaction between internally and externally oriented brain networks.

The human brain seamlessly integrates internally generated thoughts with incoming sensory information, yet the networks supporting internal (default network, DN) and external (dorsal attention network, dATN) processing are traditionally viewed as antagonistic. This raises a crucial question: how does the brain integrate information between these seemingly opposed systems? Here, using precision neuroimaging methods, we show that these internal/external networks are not as dissociated as traditionally thought. Using densely-sampled 7T fMRI data, we defined individualized whole-brain networks from participants at rest and calculated the retinotopic preferences of individual voxels within these networks during an visual mapping task. We show that while the overall network activity between the DN and dATN is independent at rest, considering a latent retinotopic code reveals a complex, voxel-scale interaction stratified by visual responsiveness. Specifically, the interaction between the DN and dATN at rest is structured at the voxel-level by each voxel's retinotopic preferences, such that the spontaneous activity of voxels preferring similar visual field locations is more anti-correlated than that of voxels preferring different visual field locations. Further, this retinotopic scaffold integrates with the domain-specific preferences of subregions within these networks, enabling efficient, parallel processing of retinotopic and domain-specific information. Thus, DN and dATN are not independent at rest: voxel-scale interaction between these networks preserves and encodes information in both positive and negative BOLD responses, even in the absence of visual input or task demands. These findings suggest that retinotopic coding may serve as a fundamental organizing principle for brain-wide communication, providing a new framework for understanding how the brain balances and integrates internal cognition with external perception.

Altered callosal morphology in post-stroke cognitive impairment

Stroke is the second leading cause of death and cognitive impairment. Post-stroke cognitive impairment (PSCI) is one of the most common sequelae among stroke survivors, yet its underlying neural mechanisms remain largely unclear. The corpus callosum (CC) plays a crucial role in interhemispheric integration and hemispheric segregation, with changes in CC morphology potentially overlapping with the spectrum of PSCI. This study aimed to investigate the morphological changes in the CC and their diagnostic value in PSCI patients. Structural MRI, neurobehavioral, and clinical data were collected from 104 PSCI patients and 54 demographically matched healthy controls. Significant reductions in CC area, circularity, and genu thickness were observed in PSCI patients, with these changes strongly correlating with global cognitive function. Subgroup analysis revealed that CC circularity significantly decreased when lesions were located in the posterior circulation, while both CC area and circularity markedly decreased with anterior circulation lesions. Receiver Operating Characteristic analyses identified the midbody areas of the CC as having high diagnostic value, with area under the curve values of 0.748 and 0.746, respectively. Further validation analyses suggest that the transcallosal fibers in these CC subregions are connected to the premotor, dorsal attention, and frontoparietal system. These findings show that areal CC atrophy in PSCI patients, particularly in regions with transcallosal connections to the premotor cortex and frontoparietal network, parallels global cognitive impairment. This suggests that CC morphology may serve as a potential imaging marker for the diagnosis and prognosis of PSCI.

Resting-state networks in chronic tinnitus: Increased connectivity between thalamus and visual areas

Tinnitus is thought to be associated with aberrant spontaneous activity in the central nervous system. Previous resting-state fMRI findings support this hypothesis and have shown a variety of alterations in neural activity in people with tinnitus compared to people without tinnitus. However, there is little replication of findings. Therefore, the current study aimed to extend on previous findings by investigating eight common resting-state networks (i.e. auditory, default mode, sensorimotor, visual, salience, dorsal attention, frontoparietal and language networks) using a a control group (n = 36) and a group of tinnitus patients (n = 46) matched for age, sex and years of education. Hearing profiles matched up to 2kHz and had a small but significant difference between groups in the high frequency range. Functional connectivity (FC) with dorsolateral prefrontal cortex (DLPFC) was also investigated separately for the first time, as this region is proposed to be core to tinnitus distress symptoms and most often used as a stimulation target in transcranial direct current stimulation (tDCS) research. The results showed that tinnitus patients had increased FC between bilateral thalamus and right visual association cortex compared to control participants. No differences were found with DLPFC, or with any of the resting-state networks (RSN), contrary to previous studies which have reported alterations in several RSNs.

Age-related differences of the time-varying features in the brain functional connectivity and cognitive aging.

Brain functional modular organization changes with age. Considering the brain as a dynamic system, recent studies have suggested that time-varying connectivity provides more information on brain functions. However, the spontaneous reconfiguration of modular brain structures over time during aging remains poorly understood. In this study, we investigated the age-related dynamic modular reconfiguration using resting-state functional MRI data (615 participants, aged 18-88 years) from Cam-CAN. We employed a graph-based modularity analysis to investigate modular variability and the transition of nodes from one module to another in modular brain networks across the adult lifespan. Results showed that modular structure exhibits both linear and nonlinear age-related trends. The modular variability is higher in early and late adulthood, with higher modular variability in the association networks and lower modular variability in the primary networks. In addition, the whole-brain transition matrix showed that the times of transition from other networks to the dorsal attention network were the largest. Furthermore, the modular structure was closely related to the number of cognitive components and memory-related cognitive performance, suggesting a potential contribution to flexibility cognitive function. Our findings highlighted the notable dynamic characteristics in large-scale brain networks across the adult lifespan, which enhanced our understanding of the neural substrate in various cognitions during aging. These findings also provided further evidence that dedifferentiation and compensation are the outcomes of functional brain interactions.

Functional brain changes in Mexican women with fibromyalgia

Fibromyalgia (FM) is a chronic condition marked by widespread pain, fatigue, sleep problems, cognitive decline, and other symptoms. Despite extensive research, the pathophysiology of FM remains poorly understood, complicating diagnosis and treatment, which often relies on self-report questionnaires. This study explored structural and functional brain changes in women with FM, identified potential biomarkers, and examined their relationship with FM severity. MRI data from 33 female FM patients and 33 matched healthy controls were utilized, focusing on T1-weighted MRI and resting-state fMRI scans. Functional connectivity (FC) analysis was performed using a machine learning framework to differentiate FM patients from healthy controls and predict FM symptom severity. No significant differences were found in brain structural features, such as gray matter volume, white matter volume, deformation-based morphometry, and cortical thickness. However, significant differences in FC were observed between FM patients and healthy controls, particularly in the default mode network (DMN), somatomotor network (SMN), visual network (VIS), and dorsal attention network (DAN). The FC metrics were significantly associated with FM severity. Our prediction model differentiated FM patients from healthy controls with an area under the curve of 0.65. FC measures accurately estimated FM symptom severities with a significant correlation (r = 0.45, p = 0.007). Functional connections in the DMN, VIS, and DAN were crucial in determining FM severity. These findings suggest that integrating brain FC measurements could serve as valuable biomarkers for identifying FM patients from healthy individuals and predicting FM symptom severity, improving diagnostic accuracy and facilitating the development of targeted therapeutic strategies.

Investigating the Impact of the Regularization Parameter on EEG Resting-State Source Reconstruction and Functional Connectivity Using Real and Simulated Data

Accurate EEG source localization is crucial for mapping resting-state network dynamics and it plays a key role in estimating source-level functional connectivity. However, EEG source estimation techniques encounter numerous methodological challenges, with a key one being the selection of the regularization parameter in minimum norm estimation. This choice is particularly intricate because the optimal amount of regularization for EEG source estimation may not align with the requirements of EEG connectivityanalysis, highlighting a nuanced trade-off. In this study, we employed a methodological approach to determine the optimal regularization coefficient that yields the most effective reconstruction outcomes across all simulations involving varying signal-to-noise ratios for synthetic EEG signals. To this aim, we considered three resting state networks: the Motor Network, the Visual Network, and the Dorsal Attention Network. The performance was assessed using three metrics, at different regularization parameters: the Region Localization Error, source extension, and source fragmentation. The results were validated using real functional connectivity data. We show that the best estimate of functional connectivity is obtained using 10−2, while 10−1 has to be preferred when source localization only is at target.

Distinct connectivity patterns between perception and attention-related brain networks characterize dyslexia: machine learning applied to resting-state fMRI

Diagnosis of dyslexia often occurs in late schooling years, leading to academic and psychological challenges. Furthermore, diagnosis is time-consuming, costly, and reliant on arbitrary cutoffs. On the other hand, automated algorithms hold great potential in medical and psychological diagnostics. The aim of the present study was to develop a machine learning tool for the detection of dyslexia in children based on the intrinsic connectivity patterns of different brain networks underlying perception and attention. Here, 117 children (8-12 years old; 58 females; 52 typical readers; TR and 65 children with dyslexia) completed cognitive and reading assessments and underwent 10 minutes of resting-state fMRI. Functional connectivity coefficients between 264 brain regions were used as features for machine learning. Different supervised algorithms were employed for classification of children with and without dyslexia. A classifier trained on dorsal attention network features exhibited the highest performance (accuracy .79, sensitivity .92, specificity .64). Auditory, visual, and fronto-parietal network-based classification showed intermediate accuracy levels (70-75%). These results highlight significant neurobiological differences in brain networks associated with visual attention between TR and children with dyslexia. Distinct neural integration patterns can differentiate dyslexia from typical development, which may be utilized in the future as a biomarker for the presence and/or severity of dyslexia.

Reductions in the white-gray functional connectome in preclinical Alzheimer's disease and their associations with amyloid and cognition.

The magnitudes and patterns of alterations of the white-gray matter (WM-GM) functional connectome in preclinical Alzheimer's disease (AD), and their associations with amyloid and cognition, remain unclear. We compared regional WM-GM functional connectivity (FC) and network properties in subjects with preclinical AD (or AD dementia) and controls (total n=344). Their associations with positron emission tomography AV45-measured amyloid beta (Aβ) load and modified Preclinical Alzheimer Cognitive Composite (mPACC) scores were examined. Preclinical AD subjects showed lower FC in specific WM-GM pairs and reduced segregation of control, dorsal attention, and somatomotor networks. A major portion of the reduced FC and network segregations were linked to elevated Aβ. Reduced FC of one WM-GM pair correlated with impaired mPACC. AD dementia exhibited broader reductions and stronger associations. The WM-GM functional connectome undergoes regional and systemic dysfunctions as early as in the preclinical stage, correlating with amyloid deposition and predicting cognitive impairment. Preclinical Alzheimer's disease (AD) subjects showed lower functional connectivity in specific white-gray matter (WM-GM) pairs and reduced segregations of control, dorsal attention, and somatomotor networks. A major portion of the reduced connectivity and network segregations were linked to elevated amyloid beta load. Only one WM-GM pair's reduced connectivity was linearly correlated with impaired cognitive composite scores. AD dementia showed more extensive reductions in connectivity, network integration, and segregation, with stronger associations with amyloid elevation and cognitive impairment. The WM-GM functional connectome offers a distinct perspective for understanding changes in brain functional architecture throughout the AD continuum.

Connectome-based Predictive Models of General and Specific Cognitive Control.

Cognitive control, the ability to adapt thoughts and actions to shifting contexts and goals, is composed primarily of three distinct yet interrelated components: Inhibition, Shifting, and Updating. While prior research has examined the nature of different cognitive components as well as their inter-relationships, fewer studies examined whole-brain connectivity to predict individual differences for the three cognitive components and associated tasks. Here, using the Connectome-based Predictive Modelling (CPM) approach and open-access data from the Human Connectome Project, we built brain network models to successfully predict individual performance differences on the Flanker task, the Dimensional Change Card Sort task, and the 2-Back task, each putatively corresponding to Inhibition, Shifting, and Updating. We focused on grayordinate fMRI data collected during the 2-Back tasks after confirming superior predictive performance over resting-state and volumetric data. High cross-task prediction accuracy as well as joint recruitment of canonical networks, such as the frontoparietal and default-mode networks, suggest the existence of a common cognitive control factor. To directly investigate the relationships among the three cognitive control components, we developed new measures to disentangle their shared and unique aspects. Our analysis confirmed that a shared control component can be well predicted from functional connectivity patterns densely located around the frontoparietal, default-mode and dorsal attention networks. In contrast, the Shifting-specific and Inhibition-specific components exhibited lower cross-prediction performance, indicating their distinct and specialized roles. Notably, the Updating-specific component showed significant cross-prediction with the general control factor, suggesting its central role in cognitive control. Given the limitation that individual behavioral measures do not purely reflect the intended cognitive constructs, our study demonstrates the need to distinguish between common and specific components of cognitive control.

Greater baseline cortical atrophy in the dorsal attention network predicts faster clinical decline in Posterior Cortical Atrophy.

Posterior Cortical Atrophy (PCA) is a clinical syndrome characterized by progressive visuospatial and visuoperceptual impairment. As the neurodegenerative disease progresses, patients lose independent functioning due to the worsening of initial symptoms and development of symptoms in other cognitive domains. The timeline of clinical progression is variable across patients, and the field currently lacks robust methods for prognostication. Here, evaluated the utility of MRI-based cortical atrophy as a predictor of longitudinal clinical decline in a sample of PCA patients. PCA patients were recruited through the Massachusetts General Hospital Frontotemporal Disorders Unit PCA Program. All patients had cortical thickness estimates from baseline MRI scans, which were used to predict longitudinal change in clinical impairment assessed by the CDR Sum-of-Boxes (CDR-SB) score. Multivariable linear regression was used to estimate the magnitude of cortical atrophy in PCA patients relative to a group of amyloid-negative cognitively unimpaired participants. Linear mixed-effects models were used to test hypotheses about the utility of baseline cortical atrophy for predicting longitudinal clinical decline. Data acquired from 34 PCA patients (mean age = 65.41 ± 7.90, 71% females) and 24 controls (mean age = 67.34 ± 4.93, 50% females) were analyzed. Sixty-two percent of the PCA patients were classified as having mild cognitive impairment (CDR 0.5) at baseline, with the rest having mild dementia (CDR 1). Each patient had at least one clinical follow-up, with the mean duration of 2.78 ± 1.62 years. Relative to controls, PCA patients showed prominent baseline atrophy in the posterior cortical regions, with the largest effect size observed in the visual network of the cerebral cortex. Cortical atrophy localized to the dorsal attention network, which supports higher-order visuospatial function, selectively predicted the rate of subsequent clinical decline. These results demonstrate the utility of a snapshot measure of cortical atrophy of the dorsal attention network for predicting the rate of subsequent clinical decline in PCA. If replicated, this topographically-specific MRI-based biomarker could be useful as a clinical prognostication tool that facilitates personalized care planning.

Functional connectivity in resting-state networks relates to short-term global cognitive functioning in cardiac arrest survivors.

Long-term cognitive impairment is common in cardiac arrest survivors. Screening to identify patients at risk is recommended. Functional magnetic resonance brain imaging (fMRI) holds potential to contribute to prediction of cognitive outcomes. In this study, we investigated the possible value of early changes in resting-state networks for predicting short and long-term cognitive functioning of cardiac arrest survivors. We performed a prospective multicenter cohort study in cardiac arrest survivors in three Dutch hospitals. Resting-state fMRI scans were acquired within a month after cardiac arrest. We primarily focused on functional connectivity within the default-mode network (DMN) and salience network (SN), and additionally explored functional connectivity in seven other networks. Cognitive outcome was measured using the Montreal Cognitive Assessment (MoCA) during hospital admission and at 3 and 12 months, and by neuropsychological examination (NPE) at 12 months. We tested mixed effects models to evaluate the value of connectivity within the networks for predicting global cognitive outcomes at the three time points, and long-term cognitive outcomes in the memory, attention, and executive functioning domains. We included 80 patients (age 60 ± 11 years, 72 (90%) male). MoCA scores increased significantly between hospital admission and 3 months (ΔMoCAhospital-3M = 2.89, p < 0.01), but not between 3 and 12 months (ΔMoCA3M-12M = 0.38, p = 0.52). Connectivity within the DMN, SN, and dorsal attention network (DAN) was positively related to global cognitive functioning during hospital admission (βDMN = 0.85, p = 0.03; βSN = 1.48, p < 0.01; βDAN = 0.96, p = 0.01), but not at 3 and 12 months. Network connectivity was also unrelated to long-term memory, attention, or executive functioning. Resting-state functional connectivity in the DMN, SN, and DAN measured in the first month after cardiac arrest is related to short-term global, but not long-term global or domain-specific cognitive performance of survivors. These results do not support the value of functional connectivity within these RSNs for prediction of long-term cognitive performance after cardiac arrest.

Enhancing cognitive performance prediction by white matter hyperintensity connectivity assessment.

White matter hyperintensities of presumed vascular origin (WMH) are associated with cognitive impairment and are a key imaging marker in evaluating brain health. However, WMH volume alone does not fully account for the extent of cognitive deficits and the mechanisms linking WMH to these deficits remain unclear. Lesion network mapping (LNM) enables to infer if brain networks are connected to lesions and could be a promising technique for enhancing our understanding of the role of WMH in cognitive disorders. Our study employed LNM to test the following hypotheses: (1) LNM-informed markers surpass WMH volumes in predicting cognitive performance, and (2) WMH contributing to cognitive impairment map to specific brain networks. We analyzed cross-sectional data of 3,485 patients from 10 memory clinic cohorts within the Meta VCI Map Consortium, using harmonized test results in 4 cognitive domains and WMH segmentations. WMH segmentations were registered to a standard space and mapped onto existing normative structural and functional brain connectome data. We employed LNM to quantify WMH connectivity to 480 atlas-based gray and white matter regions of interest (ROI), resulting in ROI-level structural and functional LNM scores. We compared the capacity of total and regional WMH volumes and LNM scores in predicting cognitive function using ridge regression models in a nested cross-validation. LNM scores predicted performance in three cognitive domains (attention/executive function, information processing speed, and verbal memory) significantly better than WMH volumes. LNM scores did not improve prediction for language functions. ROI-level analysis revealed that higher LNM scores, representing greater connectivity to WMH, in gray and white matter regions of the dorsal and ventral attention networks were associated with lower cognitive performance. Measures of WMH-related brain network connectivity significantly improve the prediction of current cognitive performance in memory clinic patients compared to WMH volume as a traditional imaging marker of cerebrovascular disease. This highlights the crucial role of network integrity, particularly in attention-related brain regions, improving our understanding of vascular contributions to cognitive impairment. Moving forward, refining WMH information with connectivity data could contribute to patient-tailored therapeutic interventions and facilitate the identification of subgroups at risk of cognitive disorders.