Unified topological inference for brain networks in temporal lobe epilepsy using the Wasserstein distance.

Persistent homology offers a powerful tool for extracting hidden topological signals from brain networks. It captures the evolution of topological structures across multiple scales, known as filtrations, thereby revealing topological features that persist over these scales. These features are summarized in persistence diagrams, and their dissimilarity is quantified using the Wasserstein distance. However, the Wasserstein distance does not follow a known distribution, posing challenges for the application of existing parametric statistical models. To tackle this issue, we introduce a unified topological inference framework centered on the Wasserstein distance. Our approach has no explicit model and distributional assumptions. The inference is performed in a completely data driven fashion. We apply this method to resting-state functional magnetic resonance images (rs-fMRI) of temporal lobe epilepsy patients collected from two different sites: the University of Wisconsin-Madison and the Medical College of Wisconsin. Importantly, our topological method is robust to variations due to sex and image acquisition, obviating the need to account for these variables as nuisance covariates. We successfully localize the brain regions that contribute the most to topological differences. A MATLAB package used for all analyses in this study is available at https://github.com/laplcebeltrami/PH-STAT.

T1-/T2-weighted ratio reveals no alterations to gray matter myelination in temporal lobe epilepsy.

Short-range functional connectivity in the limbic network is increased in patients with temporal lobe epilepsy (TLE), and recent studies have shown that cortical myelin content correlates with fMRI connectivity. We thus hypothesized that myelin may increase progressively in the epileptic network. We compared T1w/T2w gray matter myelin maps between TLE patients and age-matched controls and assessed relationships between myelin and aging. While both TLE patients and healthy controls exhibited increased T1w/T2w intensity with age, we found no evidence for significant group-level aberrations in overall myelin content or myelin changes through time in TLE.

Impaired salience network switching in psychopathy.

Growing evidence suggests that psychopathy is related to altered connectivity within and between three large-scale brain networks that support core cognitive functions, including allocation of attention. In healthy individuals, default mode network (DMN) is involved in internally-focused attention and cognition such as self-reference. Frontoparietal network (FPN) is anticorrelated with DMN and is involved in externally-focused attention to cognitively demanding tasks. A third network, salience network (SN), is involved in detecting salient cues and, crucially, appears to play a role in switching between the two anticorrelated networks, DMN and FPN, to efficiently allocate attentional resources. Psychopathy has been related to reduced anticorrelation between DMN and FPN, suggesting SN's role in switching between these two networks may be diminished in the disorder. To test this hypothesis, we used independent component analysis to derive DMN, FPN, and SN activity in resting-state fMRI data in a sample of incarcerated men (N = 148). We entered the activity of the three networks into dynamic causal modeling to test SN's switching role. The previously established switching effect of SN among young, healthy adults was replicated in a group of low psychopathy participants (posterior model probability = 0.38). As predicted, SN's switching role was significantly diminished in high psychopathy participants (t(145) = 26.39, p < .001). These findings corroborate a novel theory of brain function in psychopathy. Future studies may use this model to test whether disrupted SN switching is related to high psychopathy individuals' abnormal allocation of attention.

Association of neighborhood deprivation with white matter connectome abnormalities in temporal lobe epilepsy.

OBJECTIVE: Social determinants of health, including the effects of neighborhood disadvantage, impact epilepsy prevalence, treatment, and outcomes. This study characterized the association between aberrant white matter connectivity in temporal lobe epilepsy (TLE) and disadvantage using a US census-based neighborhood disadvantage metric, the Area Deprivation Index (ADI), derived from measures of income, education, employment, and housing quality. METHODS: Participants including 74 TLE patients (47 male, mean age = 39.2 years) and 45 healthy controls (27 male, mean age = 31.9 years) from the Epilepsy Connectome Project were classified into ADI-defined low and high disadvantage groups. Graph theoretic metrics were applied to multishell connectome diffusion-weighted imaging (DWI) measurements to derive 162 × 162 structural connectivity matrices (SCMs). The SCMs were harmonized using neuroCombat to account for interscanner differences. Threshold-free network-based statistics were used for analysis, and findings were correlated with ADI quintile metrics. A decrease in cross-sectional area (CSA) indicates reduced white matter integrity. RESULTS: Sex- and age-adjusted CSA in TLE groups was significantly reduced compared to controls regardless of disadvantage status, revealing discrete aberrant white matter tract connectivity abnormalities in addition to apparent differences in graph measures of connectivity and network-based statistics. When comparing broadly defined disadvantaged TLE groups, differences were at trend level. Sensitivity analyses of ADI quintile extremes revealed significantly lower CSA in the most compared to least disadvantaged TLE group. SIGNIFICANCE: Our findings demonstrate (1) the general impact of TLE on DWI connectome status is larger than the association with neighborhood disadvantage; however, (2) neighborhood disadvantage, indexed by ADI, revealed modest relationships with white matter structure and integrity on sensitivity analysis in TLE. Further studies are needed to explore this relationship and determine whether the white matter relationship with ADI is driven by social drift or environmental influences on brain development. Understanding the etiology and course of the disadvantage-brain integrity relationship may serve to inform care, management, and policy for patients.

The presence, nature and network characteristics of behavioural phenotypes in temporal lobe epilepsy.

The relationship between temporal lobe epilepsy and psychopathology has had a long and contentious history with diverse views regarding the presence, nature and severity of emotional-behavioural problems in this patient population. To address these controversies, we take a new person-centred approach through the application of unsupervised machine learning techniques to identify underlying latent groups or behavioural phenotypes. Addressed are the distinct psychopathological profiles, their linked frequency, patterns and severity and the disruptions in morphological and network properties that underlie the identified latent groups. A total of 114 patients and 83 controls from the Epilepsy Connectome Project were administered the Achenbach System of Empirically Based Assessment inventory from which six Diagnostic and Statistical Manual of Mental Disorders-oriented scales were analysed by unsupervised machine learning analytics to identify latent patient groups. Identified clusters were contrasted to controls as well as to each other in order to characterize their association with sociodemographic, clinical epilepsy and morphological and functional imaging network features. The concurrent validity of the behavioural phenotypes was examined through other measures of behaviour and quality of life. Patients overall exhibited significantly higher (abnormal) scores compared with controls. However, cluster analysis identified three latent groups: (i) unaffected, with no scale elevations compared with controls (Cluster 1, 37%); (ii) mild symptomatology characterized by significant elevations across several Diagnostic and Statistical Manual of Mental Disorders-oriented scales compared with controls (Cluster 2, 42%); and (iii) severe symptomatology with significant elevations across all scales compared with controls and the other temporal lobe epilepsy behaviour phenotype groups (Cluster 3, 21%). Concurrent validity of the behavioural phenotype grouping was demonstrated through identical stepwise links to abnormalities on independent measures including the National Institutes of Health Toolbox Emotion Battery and quality of life metrics. There were significant associations between cluster membership and sociodemographic (handedness and education), cognition (processing speed), clinical epilepsy (presence and lifetime number of tonic-clonic seizures) and neuroimaging characteristics (cortical volume and thickness and global graph theory metrics of morphology and resting-state functional MRI). Increasingly dispersed volumetric abnormalities and widespread disruptions in underlying network properties were associated with the most abnormal behavioural phenotype. Psychopathology in these patients is characterized by a series of discrete latent groups that harbour accompanying sociodemographic, clinical and neuroimaging correlates. The underlying neurobiological patterns suggest that the degree of psychopathology is linked to increasingly dispersed abnormal brain networks. Similar to cognition, machine learning approaches support a novel developing taxonomy of the comorbidities of epilepsy.

Multi-shell connectome DWI-based graph theory measures for the prediction of temporal lobe epilepsy and cognition.

Temporal lobe epilepsy (TLE) is the most common epilepsy syndrome that empirically represents a network disorder, which makes graph theory (GT) a practical approach to understand it. Multi-shell diffusion-weighted imaging (DWI) was obtained from 89 TLE and 50 controls. GT measures extracted from harmonized DWI matrices were used as factors in a support vector machine (SVM) analysis to discriminate between groups, and in a k-means algorithm to find intrinsic structural phenotypes within TLE. SVM was able to predict group membership (mean accuracy = 0.70, area under the curve (AUC) = 0.747, Brier score (BS) = 0.264) using 10-fold cross-validation. In addition, k-means clustering identified 2 TLE clusters: 1 similar to controls, and 1 dissimilar. Clusters were significantly different in their distribution of cognitive phenotypes, with the Dissimilar cluster containing the majority of TLE with cognitive impairment (χ2 = 6.641, P = 0.036). In addition, cluster membership showed significant correlations between GT measures and clinical variables. Given that SVM classification seemed driven by the Dissimilar cluster, SVM analysis was repeated to classify Dissimilar versus Similar + Controls with a mean accuracy of 0.91 (AUC = 0.957, BS = 0.189). Altogether, the pattern of results shows that GT measures based on connectome DWI could be significant factors in the search for clinical and neurobehavioral biomarkers in TLE.

Cortisol effects on brain functional connectivity during emotion processing in women with depression.

BACKGROUND: Depression is associated with altered functional connectivity and altered cortisol sensitivity, but the effects of cortisol on functional connectivity in depression are unknown. Previous research shows that brief cortisol augmentation (CORT) has beneficial neurocognitive effects in depression. METHODS: We investigated the effects of CORT (20mg oral cortisol) on functional connectivity during emotion processing in women with depression. Participants included 75 women with no depression or a depressive disorder. In a double-blind, crossover study, we used functional magnetic resonance imaging to measure effects of CORT vs. placebo on task-based functional connectivity during presentation of emotionally-laden images. We performed psychophysiological interaction (PPI) to test interactions among depression severity, cortisol administration, and task-dependent functional connectivity using the hippocampus and amygdala as seeds. RESULTS: During the presentation of negative images, CORT (vs. placebo) increased functional connectivity between the hippocampus and putamen in association with depression severity. During the presentation of positive pictures CORT increased functional connectivity between the hippocampus and middle frontal gyrus as well as superior temporal gyrus in association with depression. LIMITATIONS: Because cortisol was pharmacologically manipulated, results cannot be extrapolated to endogenous increases in cortisol levels. The sample did not permit investigation of differences due to race, ethnicity, or sex. Co-morbidities such as anxiety or PTSD were not accounted for. CONCLUSIONS: The results suggest that CORT has normalizing effects on task-dependent functional connectivity in women with depression during emotion processing. Increasing cortisol availability or signaling may have therapeutic benefits within affective disorders.

Regional and global resting-state functional MR connectivity in temporal lobe epilepsy: Results from the Epilepsy Connectome Project.

Temporal lobe epilepsy (TLE) has been conceptualized as focal disease with a discrete neurobiological focus and can respond well to targeted resection or ablation. In contrast, the neuro-cognitive deficits resulting from TLE can be widespread involving regions beyond the primary epileptic network. We hypothesize that this seemingly paradoxical findings can be explained by differences in connectivity between the primary epileptic region which is hyper-connected and its secondary influence on global connectome organization. This hypothesis is tested using regional and global graph theory metrics where we anticipate that regional mesial-temporal hyperconnectivity will be found and correlate with seizure frequency while global networks will be disorganized and be more closely associated with neuro-cognitive deficits. Resting-state fMRI was used to examine temporal lobe regional connectivity and global functional connectivity from 102 patients with TLE and 55 controls. Connectivity matrices were calculated for subcortical volumes and cortical parcellations. Graph theory metrics (global clustering coefficient (GCC), degree, closeness) were compared between groups and in relation to neuropsychological profiles and disease covariates using permutation testing and causal analysis. In TLE there was a decrease in GCC (p = 0.0345) associated with a worse neuropsychological profile (p = 0.0134). There was increased connectivity in the left hippocampus/amygdala (degree p = 0.0103, closeness p = 0.0104) and a decrease in connectivity in the right lateral temporal lobe (degree p = 0.0186, closeness p = 0.0122). A ratio between the hippocampus/amygdala and lateral temporal lobe-temporal lobe connectivity ratio (TLCR) revealed differences between TLE and controls for closeness (left p = 0.00149, right p = 0.0494) and for degree on left p = 0.00169; with trend on right p = 0.0567. Causal analysis suggested that "Epilepsy Activity" (seizure frequency, anti-seizure medications) was associated with increase in TLCR but not in GCC, while cognitive decline was associated with decreased GCC. These findings support the hypothesis that in TLE there is hyperconnectivity in the hippocampus/amygdala and hypoconnectivity in the lateral temporal lobe associated with "Epilepsy Activity." While, global connectome disorganization was associated with worse neuropsychological phenotype.

Network, clinical and sociodemographic features of cognitive phenotypes in temporal lobe epilepsy.

This study explored the taxonomy of cognitive impairment within temporal lobe epilepsy and characterized the sociodemographic, clinical and neurobiological correlates of identified cognitive phenotypes. 111 temporal lobe epilepsy patients and 83 controls (mean ages 33 and 39, 57% and 61% female, respectively) from the Epilepsy Connectome Project underwent neuropsychological assessment, clinical interview, and high resolution 3T structural and resting-state functional MRI. A comprehensive neuropsychological test battery was reduced to core cognitive domains (language, memory, executive, visuospatial, motor speed) which were then subjected to cluster analysis. The resulting cognitive subgroups were compared in regard to sociodemographic and clinical epilepsy characteristics as well as variations in brain structure and functional connectivity. Three cognitive subgroups were identified (intact, language/memory/executive function impairment, generalized impairment) which differed significantly, in a systematic fashion, across multiple features. The generalized impairment group was characterized by an earlier age at medication initiation (P < 0.05), fewer patient (P < 0.001) and parental years of education (P < 0.05), greater racial diversity (P < 0.05), and greater number of lifetime generalized seizures (P < 0.001). The three groups also differed in an orderly manner across total intracranial (P < 0.001) and bilateral cerebellar cortex volumes (P < 0.01), and rate of bilateral hippocampal atrophy (P < 0.014), but minimally in regional measures of cortical volume or thickness. In contrast, large-scale patterns of cortical-subcortical covariance networks revealed significant differences across groups in global and local measures of community structure and distribution of hubs. Resting-state fMRI revealed stepwise anomalies as a function of cluster membership, with the most abnormal patterns of connectivity evident in the generalized impairment group and no significant differences from controls in the cognitively intact group. Overall, the distinct underlying cognitive phenotypes of temporal lobe epilepsy harbor systematic relationships with clinical, sociodemographic and neuroimaging correlates. Cognitive phenotype variations in patient and familial education and ethnicity, with linked variations in total intracranial volume, raise the question of an early and persisting socioeconomic-status related neurodevelopmental impact, with additional contributions of clinical epilepsy factors (e.g., lifetime generalized seizures). The neuroimaging features of cognitive phenotype membership are most notable for disrupted large scale cortical-subcortical networks and patterns of functional connectivity with bilateral hippocampal and cerebellar atrophy. The cognitive taxonomy of temporal lobe epilepsy appears influenced by features that reflect the combined influence of socioeconomic, neurodevelopmental and neurobiological risk factors.

Neuroticism in temporal lobe epilepsy is associated with altered limbic-frontal lobe resting-state functional connectivity.

Neuroticism, a core personality trait characterized by a tendency towards experiencing negative affect, has been reported to be higher in people with temporal lobe epilepsy (TLE) compared with healthy individuals. Neuroticism is a known predictor of depression and anxiety, which also occur more frequently in people with TLE. The purpose of this study was to identify abnormalities in whole-brain resting-state functional connectivity in relation to neuroticism in people with TLE and to determine the degree of unique versus shared patterns of abnormal connectivity in relation to elevated symptoms of depression and anxiety. Ninety-three individuals with TLE (55 females) and 40 healthy controls (18 females) from the Epilepsy Connectome Project (ECP) completed measures of neuroticism, depression, and anxiety, which were all significantly higher in people with TLE compared with controls. Resting-state functional connectivity was compared between controls and groups with TLE with high and low neuroticism using analysis of variance (ANOVA) and t-test. In secondary analyses, the same analytics were performed using measures of depression and anxiety and the unique variance in resting-state connectivity associated with neuroticism independent of symptoms of depression and anxiety identified. Increased neuroticism was significantly associated with hyposynchrony between the right hippocampus and Brodmann area (BA) 9 (region of prefrontal cortex (PFC)) (p < 0.005), representing a unique relationship independent of symptoms of depression and anxiety. Hyposynchrony of connection between the right hippocampus and BA47 (anterior frontal operculum) was associated with high neuroticism and with higher depression and anxiety scores (p < 0.05), making it a shared abnormal connection for the three measures. In conclusion, increased neuroticism exhibits both unique and shared patterns of abnormal functional connectivity with depression and anxiety symptoms between regions of the mesial temporal and frontal lobe.

Brain aging in temporal lobe epilepsy: Chronological, structural, and functional.

The association of epilepsy with structural brain changes and cognitive abnormalities in midlife has raised concern regarding the possibility of future accelerated brain and cognitive aging and increased risk of later life neurocognitive disorders. To address this issue we examined age-related processes in both structural and functional neuroimaging among individuals with temporal lobe epilepsy (TLE, N = 104) who were participants in the Epilepsy Connectome Project (ECP). Support vector regression (SVR) models were trained from 151 healthy controls and used to predict TLE patients' brain ages. It was found that TLE patients on average have both older structural (+6.6 years) and functional (+8.3 years) brain ages compared to healthy controls. Accelerated functional brain age (functional - chronological age) was mildly correlated (corrected P = 0.07) with complex partial seizure frequency and the number of anti-epileptic drug intake. Functional brain age was a significant correlate of declining cognition (fluid abilities) and partially mediated chronological age-fluid cognition relationships. Chronological age was the only positive predictor of crystallized cognition. Accelerated aging is evident not only in the structural brains of patients with TLE, but also in their functional brains. Understanding the causes of accelerated brain aging in TLE will be clinically important in order to potentially prevent or mitigate their cognitive deficits.

Neuroanatomical correlates of personality traits in temporal lobe epilepsy: Findings from the Epilepsy Connectome Project.

Behavioral and personality disorders in temporal lobe epilepsy (TLE) have been a topic of interest and controversy for decades, with less attention paid to alterations in normal personality structure and traits. In this investigation, core personality traits (the Big 5) and their neurobiological correlates in TLE were explored using the Neuroticism Extraversion Openness-Five Factor Inventory (NEO-FFI) and structural magnetic resonance imaging (MRI) through the Epilepsy Connectome Project (ECP). NEO-FFI scores from 67 individuals with TLE (34.6 ±â€¯9.5 years; 67% women) were compared to 31 healthy controls (32.8 ±â€¯8.9 years; 41% women) to assess differences in the Big 5 traits (agreeableness, openness, conscientiousness, neuroticism, and extraversion). Individuals with TLE showed significantly higher neuroticism, with no significant differences on the other traits. Neural correlates of neuroticism were then determined in participants with TLE including cortical and subcortical volumes. Distributed reductions in cortical gray matter volumes were associated with increased neuroticism. Subcortically, hippocampal and amygdala volumes were negatively associated with neuroticism. These results offer insight into alterations in the Big 5 personality traits in TLE and their brain-related correlates.

Effective Connectivity Within the Default Mode Network in Left Temporal Lobe Epilepsy: Findings from the Epilepsy Connectome Project.

The Epilepsy Connectome Project examines the differences in connectomes between temporal lobe epilepsy (TLE) patients and healthy controls. Using these data, the effective connectivity of the default mode network (DMN) in patients with left TLE compared with healthy controls was investigated using spectral dynamic causal modeling (spDCM) of resting-state functional magnetic resonance imaging data. Group comparisons were made using two parametric empirical Bayes (PEB) models. The first level of each PEB model consisted of each participant's spDCM. Two different second-level models were constructed: the first comparing effective connectivity of the groups directly and the second using the Rey Auditory Verbal Learning Test (RAVLT) delayed free recall index as a covariate at the second level to assess effective connectivity controlling for the poor memory performance of left TLE patients. After an automated search over the nested parameter space and thresholding parameters at 95% posterior probability, both models revealed numerous connections in the DMN, which lead to inhibition of the left hippocampal formation. Left hippocampal formation inhibition may be an inherent result of the left temporal epileptogenic focus as memory differences were controlled for in one model and the same connections remained. An excitatory connection from the posterior cingulate cortex to the medial prefrontal cortex was found to be concomitant with left hippocampal formation inhibition in TLE patients when including RAVLT delayed free recall at the second level.

Bayesian convolutional neural network based MRI brain extraction on nonhuman primates.

Brain extraction or skull stripping of magnetic resonance images (MRI) is an essential step in neuroimaging studies, the accuracy of which can severely affect subsequent image processing procedures. Current automatic brain extraction methods demonstrate good results on human brains, but are often far from satisfactory on nonhuman primates, which are a necessary part of neuroscience research. To overcome the challenges of brain extraction in nonhuman primates, we propose a fully-automated brain extraction pipeline combining deep Bayesian convolutional neural network (CNN) and fully connected three-dimensional (3D) conditional random field (CRF). The deep Bayesian CNN, Bayesian SegNet, is used as the core segmentation engine. As a probabilistic network, it is not only able to perform accurate high-resolution pixel-wise brain segmentation, but also capable of measuring the model uncertainty by Monte Carlo sampling with dropout in the testing stage. Then, fully connected 3D CRF is used to refine the probability result from Bayesian SegNet in the whole 3D context of the brain volume. The proposed method was evaluated with a manually brain-extracted dataset comprising T1w images of 100 nonhuman primates. Our method outperforms six popular publicly available brain extraction packages and three well-established deep learning based methods with a mean Dice coefficient of 0.985 and a mean average symmetric surface distance of 0.220 mm. A better performance against all the compared methods was verified by statistical tests (all p-values < 10-4, two-sided, Bonferroni corrected). The maximum uncertainty of the model on nonhuman primate brain extraction has a mean value of 0.116 across all the 100 subjects. The behavior of the uncertainty was also studied, which shows the uncertainty increases as the training set size decreases, the number of inconsistent labels in the training set increases, or the inconsistency between the training set and the testing set increases.

Delineating potential epileptogenic areas utilizing resting functional magnetic resonance imaging (fMRI) in epilepsy patients.

Seizure localization includes neuroimaging like electroencephalogram, and magnetic resonance imaging (MRI) with limited ability to characterize the epileptogenic network. Temporal clustering analysis (TCA) characterizes epileptogenic network congruent with interictal epileptiform discharges by clustering together voxels with transient signals. We generated epileptogenic areas for 12 of 13 epilepsy patients with TCA, congruent with different areas of seizure onset. Resting functional MRI (fMRI) scans are noninvasive, and can be acquired quickly, in patients with different levels of severity and function. Analyzing resting fMRI data using TCA is quick and can complement clinical methods to characterize the epileptogenic network.

Age-Related Changes in BOLD Activation Pattern in Phonemic Fluency Paradigm: An Investigation of Activation, Functional Connectivity and Psychophysiological Interactions.

Healthy aging is associated with decline of cognitive functions. However, even before those declines become noticeable, the neural architecture underlying those mechanisms has undergone considerable restructuring and reorganization. During performance of a cognitive task, not only have the task-relevant networks demonstrated reorganization with aging, which occurs primarily by recruitment of additional areas to preserve performance, but the task-irrelevant network of the "default-mode" network (DMN), which is normally deactivated during task performance, has also consistently shown reduction of this deactivation with aging. Here, we revisited those age-related changes in task-relevant (i.e., language system) and task-irrelevant (i.e., DMN) systems with a language production paradigm in terms of task-induced activation/deactivation, functional connectivity, and context-dependent correlations between the two systems. Our task fMRI data demonstrated a late increase in cortical recruitment in terms of extent of activation, only observable in our older healthy adult group, when compared to the younger healthy adult group, with recruitment of the contralateral hemisphere, but also other regions from the network previously underutilized. Our middle-aged individuals, when compared to the younger healthy adult group, presented lower levels of activation intensity and connectivity strength, with no recruitment of additional regions, possibly reflecting an initial, uncompensated, network decline. In contrast, the DMN presented a gradual decrease in deactivation intensity and deactivation extent (i.e., low in the middle-aged, and lower in the old) and similar gradual reduction of functional connectivity within the network, with no compensation. The patterns of age-related changes in the task-relevant system and DMN are incongruent with the previously suggested notion of anti-correlation of the two systems. The context-dependent correlation by psycho-physiological interaction (PPI) analysis demonstrated an independence of these two systems, with the onset of task not influencing the correlation between the two systems. Our results suggest that the language network and the DMN may be non-dependent systems, potentially correlated through the re-allocation of cortical resources, and that aging may affect those two systems differently.

Differing Patterns of Altered Slow-5 Oscillations in Healthy Aging and Ischemic Stroke.

The 'default-mode' network (DMN) has been investigated in the presence of various disorders, such as Alzheimer's disease and Autism spectrum disorders. More recently, this investigation has expanded to include patients with ischemic injury. Here, we characterized the effects of ischemic injury in terms of its spectral distribution of resting-state low-frequency oscillations and further investigated whether those specific disruptions were unique to the DMN, or rather more general, affecting the global cortical system. With 43 young healthy adults, 42 older healthy adults, 14 stroke patients in their early stage (<7 days after stroke onset), and 16 stroke patients in their later stage (between 1 to 6 months after stroke onset), this study showed that patterns of cortical system disruption may differ between healthy aging and following the event of an ischemic stroke. The stroke group in the later stage demonstrated a global reduction in the amplitude of the slow-5 oscillations (0.01-0.027 Hz) in the DMN as well as in the primary visual and sensorimotor networks, two 'task-positive' networks. In comparison to the young healthy group, the older healthy subjects presented a decrease in the amplitude of the slow-5 oscillations specific to the components of the DMN, while exhibiting an increase in oscillation power in the task-positive networks. These two processes of a decrease DMN and an increase in 'task-positive' slow-5 oscillations may potentially be related, with a deficit in DMN inhibition, leading to an elevation of oscillations in non-DMN systems. These findings also suggest that disruptions of the slow-5 oscillations in healthy aging may be more specific to the DMN while the disruptions of those oscillations following a stroke through remote (diaschisis) effects may be more widespread, highlighting a non-specificity of disruption on the DMN in stroke population. The mechanisms underlying those differing modes of network disruption need to be further explored to better inform our understanding of brain function in healthy individuals and following injury.

Implication of the Slow-5 Oscillations in the Disruption of the Default-Mode Network in Healthy Aging and Stroke.

The processes of normal aging and aging-related pathologies subject the brain to an active re-organization of its brain networks. Among these, the default-mode network (DMN) is consistently implicated with a demonstrated reduction in functional connectivity within the network. However, no clear stipulation on the underlying mechanisms of the de-synchronization has yet been provided. In this study, we examined the spectral distribution of the intrinsic low-frequency oscillations (LFOs) of the DMN sub-networks in populations of young normals, older subjects, and acute and subacute ischemic stroke patients. The DMN sub-networks were derived using a mid-order group independent component analysis with 117 eyes-closed resting-state functional magnetic resonance imaging (rs-fMRI) sessions from volunteers in those population groups, isolating three robust components of the DMN among other resting-state networks. The posterior component of the DMN presented noticeable differences. Measures of amplitude of low-frequency fluctuation (ALFF) and fractional ALFF (fALFF) of the network component demonstrated a decrease in resting-state cortical oscillation power in the elderly (normal and patient), specifically in the slow-5 (0.01-0.027 Hz) range of oscillations. Furthermore, the contribution of the slow-5 oscillations during the resting state was diminished for a greater influence of the slow-4 (0.027-0.073 Hz) oscillations in the subacute stroke group, not only suggesting a vulnerability of the slow-5 oscillations to disruption but also indicating a change in the distribution of the oscillations within the resting-state frequencies. The reduction of network slow-5 fALFF in the posterior DMN component was found to present a potential association with behavioral measures, suggesting a brain-behavior relationship to those oscillations, with this change in behavior potentially resulting from an altered network integrity induced by a weakening of the slow-5 oscillations during the resting state. The repeated identification of those frequencies in the disruption of DMN stresses a critical role of the slow-5 oscillations in network disruption, and it accentuates the importance of managing those oscillations in the health of the DMN.

Targeting CD146 with a 64Cu-labeled antibody enables in vivo immunoPET imaging of high-grade gliomas.

Given the highly heterogeneous character of brain malignancies and the associated implication for its proper diagnosis and treatment, finding biomarkers that better characterize this disease from a molecular standpoint is imperative. In this study, we evaluated CD146 as a potential molecular target for diagnosis and targeted therapy of glioblastoma multiforme (GBM), the most common and lethal brain malignancy. YY146, an anti-CD146 monoclonal antibody, was generated and radiolabeled for noninvasive positron-emission tomography (PET) imaging of orthotopic GBM models. (64)Cu-labeled YY146 preferentially accumulated in the tumors of mice bearing U87MG xenografts, which allowed the acquisition of high-contrast PET images of small tumor nodules (∼ 2 mm). Additionally, we found that tumor uptake correlated with the levels of CD146 expression in a highly specific manner. We also explored the potential therapeutic effects of YY146 on the cancer stem cell (CSC) and epithelial-to-mesenchymal (EMT) properties of U87MG cells, demonstrating that YY146 can mitigate those aggressive phenotypes. Using YY146 as the primary antibody, we performed histological studies of World Health Organization (WHO) grades I through IV primary gliomas. The positive correlation found between CD146-positive staining and high tumor grade (χ(2) = 9.028; P = 0.029) concurred with the GBM data available in The Cancer Genome Atlas (TCGA) and validated the clinical value of YY146. In addition, we demonstrate that YY146 can be used to detect CD146 in various cancer cell lines and human resected tumor tissues of multiple other tumor types (gastric, ovarian, liver, and lung), indicating a broad applicability of YY146 in solid tumors.

5²Mn production for PET/MRI tracking of human stem cells expressing divalent metal transporter 1 (DMT1).

There is a growing demand for long-term in vivo stem cell imaging for assessing cell therapy techniques and guiding therapeutic decisions. This work develops the production of (52)Mn and establishes proof of concept for the use of divalent metal transporter 1 (DMT1) as a positron emission tomography (PET) and magnetic resonance imaging (MRI) reporter gene for stem cell tracking in the rat brain. (52)Mn was produced via proton irradiation of a natural chromium target. In a comparison of two (52)Mn separation methods, solvent-solvent extraction was preferred over ion exchange chromatography because of reduced chromium impurities and higher (52)Mn recovery. In vitro uptake of Mn-based PET and MRI contrast agents ((52)Mn(2+) and Mn(2+), respectively) was enhanced in DMT1 over-expressing human neural progenitor cells (hNPC-DMT1) compared to wild-type control cells (hNPC-WT). After cell transplantation in the rat striatum, increased uptake of Mn-based contrast agents in grafted hNPC-DMT1 was detected in in vivo manganese-enhanced MRI (MEMRI) and ex vivo PET and autoradiography. These initial studies indicate that this approach holds promise for dual-modality PET/MR tracking of transplanted stem cells in the central nervous system and prompt further investigation into the clinical applicability of this technique.