Dynamic modulation of subthalamic nucleus activity facilitates adaptive behavior.

Adapting actions to changing goals and environments is central to intelligent behavior. There is evidence that the basal ganglia play a crucial role in reinforcing or adapting actions depending on their outcome. However, the corresponding electrophysiological correlates in the basal ganglia and the extent to which these causally contribute to action adaptation in humans is unclear. Here, we recorded electrophysiological activity and applied bursts of electrical stimulation to the subthalamic nucleus, a core area of the basal ganglia, in 16 patients with Parkinson's disease (PD) on medication using temporarily externalized deep brain stimulation (DBS) electrodes. Patients as well as 16 age- and gender-matched healthy participants attempted to produce forces as close as possible to a target force to collect a maximum number of points. The target force changed over trials without being explicitly shown on the screen so that participants had to infer target force based on the feedback they received after each movement. Patients and healthy participants were able to adapt their force according to the feedback they received (P < 0.001). At the neural level, decreases in subthalamic beta (13 to 30 Hz) activity reflected poorer outcomes and stronger action adaptation in 2 distinct time windows (Pcluster-corrected < 0.05). Stimulation of the subthalamic nucleus reduced beta activity and led to stronger action adaptation if applied within the time windows when subthalamic activity reflected action outcomes and adaptation (Pcluster-corrected < 0.05). The more the stimulation volume was connected to motor cortex, the stronger was this behavioral effect (Pcorrected = 0.037). These results suggest that dynamic modulation of the subthalamic nucleus and interconnected cortical areas facilitates adaptive behavior.

Inflammation-Triggered Enlargement of Choroid Plexus in Subacute COVID-19 Patients with Neurological Symptoms.

OBJECTIVE: To investigate whether choroid plexus volumes in subacute coronavirus disease 2019 (COVID-19) patients with neurological symptoms could indicate inflammatory activation or barrier dysfunction and assess their association with clinical data. METHODS: Choroid plexus volumes were measured in 28 subacute COVID-19 patients via cerebral magnetic resonance imaging (MRI), compared with those in infection-triggered non-COVID-19 encephalopathy patients (n = 25), asymptomatic individuals after COVID-19 (n = 21), and healthy controls (n = 21). Associations with inflammatory serum markers (peak counts of leukocytes, C-reactive protein [CRP], interleukin 6), an MRI-based marker of barrier dysfunction (CSF volume fraction [V-CSF]), and clinical parameters like olfactory performance and cognitive scores (Montreal Cognitive Assessment) were investigated. RESULTS: COVID-19 patients showed significantly larger choroid plexus volumes than control groups (p < 0.001, η2 = 0.172). These volumes correlated significantly with peak leukocyte levels (p = 0.001, Pearson's r = 0.621) and V-CSF (p = 0.009, Spearman's rho = 0.534), but neither with CRP nor interleukin 6. No significant correlations were found with clinical parameters. INTERPRETATION: In patients with subacute COVID-19, choroid plexus volume is a marker of central nervous system inflammation and barrier dysfunction in the presence of neurologic symptoms. The absence of plexus enlargement in infection-triggered non-COVID-19 encephalopathy suggests a specific severe acute respiratory syndrome coronavirus 2 effect. This study also documents an increase in choroid plexus volume for the first time as a parainfectious event. ANN NEUROL 2024.

Prognostic value of single-subject grey matter networks in early multiple sclerosis.

The identification of prognostic markers in early multiple sclerosis (MS) is challenging and requires reliable measures that robustly predict future disease trajectories. Ideally, such measures should make inferences at the individual level to inform clinical decisions. This study investigated the prognostic value of longitudinal structural networks to predict 5-year Expanded Disability Status Scale (EDSS) progression in patients with relapsing-remitting MS (RRMS). We hypothesized that network measures, derived from MRI, outperform conventional MRI measurements at identifying patients at risk of developing disability progression. This longitudinal, multicentre study within the Magnetic Resonance Imaging in MS (MAGNIMS) network included 406 patients with RRMS (mean age = 35.7 ± 9.1 years) followed up for 5 years (mean follow-up = 5.0 ± 0.6 years). EDSS was determined to track disability accumulation. A group of 153 healthy subjects (mean age = 35.0 ± 10.1 years) with longitudinal MRI served as controls. All subjects underwent MRI at baseline and again 1 year after baseline. Grey matter atrophy over 1 year and white matter lesion load were determined. A single-subject brain network was reconstructed from T1-weighted scans based on grey matter atrophy measures derived from a statistical parameter mapping-based segmentation pipeline. Key topological measures, including network degree, global efficiency and transitivity, were calculated at single-subject level to quantify network properties related to EDSS progression. Areas under receiver operator characteristic (ROC) curves were constructed for grey matter atrophy and white matter lesion load, and the network measures and comparisons between ROC curves were conducted. The applied network analyses differentiated patients with RRMS who experience EDSS progression over 5 years through lower values for network degree [H(2) = 30.0, P < 0.001] and global efficiency [H(2) = 31.3, P < 0.001] from healthy controls but also from patients without progression. For transitivity, the comparisons showed no difference between the groups [H(2) = 1.5, P = 0.474]. Most notably, changes in network degree and global efficiency were detected independent of disease activity in the first year. The described network reorganization in patients experiencing EDSS progression was evident in the absence of grey matter atrophy. Network degree and global efficiency measurements demonstrated superiority of network measures in the ROC analyses over grey matter atrophy and white matter lesion load in predicting EDSS worsening (all P-values < 0.05). Our findings provide evidence that grey matter network reorganization over 1 year discloses relevant information about subsequent clinical worsening in RRMS. Early grey matter restructuring towards lower network efficiency predicts disability accumulation and outperforms conventional MRI predictors.

Concurrent inflammation-related brain reorganization in multiple sclerosis and depression.

BACKGROUND: Neuroinflammation affects brain tissue integrity in multiple sclerosis (MS) and may have a role in major depressive disorder (MDD). Whether advanced magnetic resonance imaging characteristics of the gray-to-white matter border serve as proxy of neuroinflammatory activity in MDD and MS remain unknown. METHODS: We included 684 participants (132 MDD patients with recurrent depressive episodes (RDE), 70 MDD patients with a single depressive episode (SDE), 222 MS patients without depressive symptoms (nMS), 58 MS patients with depressive symptoms (dMS), and 202 healthy controls (HC)). 3 T-T1w MRI-derived gray-to-white matter contrast (GWc) was used to reconstruct and characterize connectivity alterations of GWc-covariance networks by means of modularity, clustering coefficient, and degree. A cross-validated support vector machine was used to test the ability of GWc to stratify groups according to their depression symptoms, measured with BDI, at the single-subject level in MS and MDD independently. FINDINGS: MS and MDD patients showed increased modularity (ANOVA partial-η2 = 0.3) and clustering (partial-η2 = 0.1) compared to HC. In the subgroups, a linear trend analysis attested a gradient of modularity increases in the form: HC, dMS, nMS, SDE, and RDE (ANOVA partial-η2 = 0.28, p < 0.001) while this trend was less evident for clustering coefficient. Reduced morphological integrity (GWc) was seen in patients with increased depressive symptoms (partial-η2 = 0.42, P < 0.001) and was associated with depression scores across patient groups (r = -0.2, P < 0.001). Depressive symptoms in MS were robustly classified (88 %). CONCLUSIONS: Similar structural network alterations in MDD and MS exist, suggesting possible common inflammatory events like demyelination, neuroinflammation that are caught by GWc analyses. These alterations may vary depending on the severity of symptoms and in the case of MS may elucidate the occurrence of comorbid depression.

Using The Virtual Brain to study the relationship between structural and functional connectivity in patients with multiple sclerosis: a multicenter study.

The relationship between structural connectivity (SC) and functional connectivity (FC) captured from magnetic resonance imaging, as well as its interaction with disability and cognitive impairment, is not well understood in people with multiple sclerosis (pwMS). The Virtual Brain (TVB) is an open-source brain simulator for creating personalized brain models using SC and FC. The aim of this study was to explore SC-FC relationship in MS using TVB. Two different model regimes have been studied: stable and oscillatory, with the latter including conduction delays in the brain. The models were applied to 513 pwMS and 208 healthy controls (HC) from 7 different centers. Models were analyzed using structural damage, global diffusion properties, clinical disability, cognitive scores, and graph-derived metrics from both simulated and empirical FC. For the stable model, higher SC-FC coupling was associated with pwMS with low Single Digit Modalities Test (SDMT) score (F=3.48, P$\lt$0.05), suggesting that cognitive impairment in pwMS is associated with a higher SC-FC coupling. Differences in entropy of the simulated FC between HC, high and low SDMT groups (F=31.57, P$\lt$1e-5), show that the model captures subtle differences not detected in the empirical FC, suggesting the existence of compensatory and maladaptive mechanisms between SC and FC in MS.

Cortical and subcortical microstructure integrity changes after repetitive transcranial magnetic stimulation therapy in cocaine use disorder and relates to clinical outcomes.

Cocaine use disorder (CUD) is a worldwide public health condition that is suggested to induce pathological changes in macrostructure and microstructure. Repetitive transcranial magnetic stimulation (rTMS) has gained attention as a potential treatment for CUD symptoms. Here, we sought to elucidate whether rTMS induces changes in white matter (WM) microstructure in frontostriatal circuits after 2 weeks of therapy in patients with CUD and to test whether baseline WM microstructure of the same circuits affects clinical improvement. This study consisted of a 2-week, parallel-group, double-blind, randomized controlled clinical trial (acute phase) (sham [n = 23] and active [n = 27]), in which patients received two daily sessions of rTMS on the left dorsolateral prefrontal cortex (lDLPFC) as an add-on treatment. T1-weighted and high angular resolution diffusion-weighted imaging (DWI-HARDI) at baseline and 2 weeks after served to evaluate WM microstructure. After active rTMS, results showed a significant increase in neurite density compared with sham rTMS in WM tracts connecting lDLPFC with left and right ventromedial prefrontal cortex (vmPFC). Similarly, rTMS showed a reduction in orientation dispersion in WM tracts connecting lDLPFC with the left caudate nucleus, left thalamus, and left vmPFC. Results also showed a greater reduction in craving Visual Analogue Scale (VAS) after rTMS when baseline intra-cellular volume fraction (ICVF) was low in WM tracts connecting left caudate nucleus with substantia nigra and left pallidum, as well as left thalamus with substantia nigra and left pallidum. Our results evidence rTMS-induced WM microstructural changes in fronto-striato-thalamic circuits and support its efficacy as a therapeutic tool in treating CUD. Further, individual clinical improvement may rely on the patient's individual structural connectivity integrity.

Perspectives of Implementation of Closed-Loop Deep Brain Stimulation: From Neurological to Psychiatric Disorders.

BACKGROUND: Deep brain stimulation (DBS) is a highly efficient, evidence-based therapy to alleviate symptoms and improve quality of life in movement disorders such as Parkinson's disease, essential tremor, and dystonia, which is also being applied in several psychiatric disorders, such as obsessive-compulsive disorder and depression, when they are otherwise resistant to therapy. SUMMARY: At present, DBS is clinically applied in the so-called open-loop approach, with fixed stimulation parameters, irrespective of the patients' clinical state(s). This approach ignores the brain states or feedback from the central nervous system or peripheral recordings, thus potentially limiting its efficacy and inducing side effects by stimulation of the targeted networks below or above the therapeutic level. KEY MESSAGES: The currently emerging closed-loop (CL) approaches are designed to adapt stimulation parameters to the electrophysiological surrogates of disease symptoms and states. CL-DBS paves the way for adaptive personalized DBS protocols. This review elaborates on the perspectives of the CL technology and discusses its opportunities as well as its potential pitfalls for both clinical and research use in neuropsychiatric disorders.

Translational value of choroid plexus imaging for tracking neuroinflammation in mice and humans.

Neuroinflammation is a pathophysiological hallmark of multiple sclerosis and has a close mechanistic link to neurodegeneration. Although this link is potentially targetable, robust translatable models to reliably quantify and track neuroinflammation in both mice and humans are lacking. The choroid plexus (ChP) plays a pivotal role in regulating the trafficking of immune cells from the brain parenchyma into the cerebrospinal fluid (CSF) and has recently attracted attention as a key structure in the initiation of inflammatory brain responses. In a translational framework, we here address the integrity and multidimensional characteristics of the ChP under inflammatory conditions and question whether ChP volumes could act as an interspecies marker of neuroinflammation that closely interrelates with functional impairment. Therefore, we explore ChP characteristics in neuroinflammation in patients with multiple sclerosis and in two experimental mouse models, cuprizone diet-related demyelination and experimental autoimmune encephalomyelitis. We demonstrate that ChP enlargement-reconstructed from MRI-is highly associated with acute disease activity, both in the studied mouse models and in humans. A close dependency of ChP integrity and molecular signatures of neuroinflammation is shown in the performed transcriptomic analyses. Moreover, pharmacological modulation of the blood-CSF barrier with natalizumab prevents an increase of the ChP volume. ChP enlargement is strongly linked to emerging functional impairment as depicted in the mouse models and in multiple sclerosis patients. Our findings identify ChP characteristics as robust and translatable hallmarks of acute and ongoing neuroinflammatory activity in mice and humans that could serve as a promising interspecies marker for translational and reverse-translational approaches.

Sunlight exposure exerts immunomodulatory effects to reduce multiple sclerosis severity.

Multiple sclerosis (MS) disease risk is associated with reduced sun-exposure. This study assessed the relationship between measures of sun exposure (vitamin D [vitD], latitude) and MS severity in the setting of two multicenter cohort studies (nNationMS = 946, nBIONAT = 990). Additionally, effect-modification by medication and photosensitivity-associated MC1R variants was assessed. High serum vitD was associated with a reduced MS severity score (MSSS), reduced risk for relapses, and lower disability accumulation over time. Low latitude was associated with higher vitD, lower MSSS, fewer gadolinium-enhancing lesions, and lower disability accumulation. The association of latitude with disability was lacking in IFN-ß-treated patients. In carriers of MC1R:rs1805008(T), who reported increased sensitivity toward sunlight, lower latitude was associated with higher MRI activity, whereas for noncarriers there was less MRI activity at lower latitudes. In a further exploratory approach, the effect of ultraviolet (UV)-phototherapy on the transcriptome of immune cells of MS patients was assessed using samples from an earlier study. Phototherapy induced a vitD and type I IFN signature that was most apparent in monocytes but that could also be detected in B and T cells. In summary, our study suggests beneficial effects of sun exposure on established MS, as demonstrated by a correlative network between the three factors: Latitude, vitD, and disease severity. However, sun exposure might be detrimental for photosensitive patients. Furthermore, a direct induction of type I IFNs through sun exposure could be another mechanism of UV-mediated immune-modulation in MS.

Dynamic flexibility and controllability of network communities in juvenile myoclonic epilepsy.

Juvenile myoclonic epilepsy (JME) is the most common syndrome within the idiopathic generalized epilepsy spectrum, manifested by myoclonic and generalized tonic-clonic seizures and spike-and-wave discharges (SWDs) on electroencephalography (EEG). Currently, the pathophysiological concepts addressing SWD generation in JME are still incomplete. In this work, we characterize the temporal and spatial organization of functional networks and their dynamic properties as derived from high-density EEG (hdEEG) recordings and MRI in 40 JME patients (25.4 ± 7.6 years, 25 females). The adopted approach allows for the construction of a precise dynamic model of ictal transformation in JME at the cortical and deep brain nuclei source levels. We implement Louvain algorithm to attribute brain regions with similar topological properties to modules during separate time windows before and during SWD generation. Afterwards, we quantify how modular assignments evolve and steer through different states towards the ictal state by measuring characteristics of flexibility and controllability. We find antagonistic dynamics of flexibility and controllability within network modules as they evolve towards and undergo ictal transformation. Prior to SWD generation, we observe concomitantly increasing flexibility (F(1,39) = 25.3, corrected p < 0.001) and decreasing controllability (F(1,39) = 55.3, p < 0.001) within the fronto-parietal module in γ-band. On a step further, during interictal SWDs as compared to preceding time windows, we notice decreasing flexibility (F(1,39) = 11.9, p < 0.001) and increasing controllability (F(1,39) = 10.1, p < 0.001) within the fronto-temporal module in γ-band. During ictal SWDs as compared to prior time windows, we demonstrate significantly decreasing flexibility (F(1,14) = 31.6; p < 0.001) and increasing controllability (F(1,14) = 44.7, p < 0.001) within the basal ganglia module. Furthermore, we show that flexibility and controllability within the fronto-temporal module of the interictal SWDs relate to seizure frequency and cognitive performance in JME patients. Our results demonstrate that detection of network modules and quantification of their dynamic properties is relevant to track the generation of SWDs. The observed flexibility and controllability dynamics reflect the reorganization of de-/synchronized connections and the ability of evolving network modules to reach a seizure-free state, respectively. These findings may advance the elaboration of network-based biomarkers and more targeted therapeutic neuromodulatory approaches in JME.

Subcortical Volumes as Early Predictors of Fatigue in Multiple Sclerosis.

OBJECTIVE: Fatigue is a frequent and severe symptom in multiple sclerosis (MS), but its pathophysiological origin remains incompletely understood. We aimed to examine the predictive value of subcortical gray matter volumes for fatigue severity at disease onset and after 4 years by applying structural equation modeling (SEM). METHODS: This multicenter cohort study included 601 treatment-naive patients with MS after the first demyelinating event. All patients underwent a standardized 3T magnetic resonance imaging (MRI) protocol. A subgroup of 230 patients with available clinical follow-up data after 4 years was also analyzed. Associations of subcortical volumes (included into SEM) with MS-related fatigue were studied regarding their predictive value. In addition, subcortical regions that have a central role in the brain network (hubs) were determined through structural covariance network (SCN) analysis. RESULTS: Predictive causal modeling identified volumes of the caudate (s [standardized path coefficient] = 0.763, p = 0.003 [left]; s = 0.755, p = 0.006 [right]), putamen (s = 0.614, p = 0.002 [left]; s = 0.606, p = 0.003 [right]) and pallidum (s = 0.606, p = 0.012 [left]; s = 0.606, p = 0.012 [right]) as prognostic factors for fatigue severity in the cross-sectional cohort. Moreover, the volume of the pons was additionally predictive for fatigue severity in the longitudinal cohort (s = 0.605, p = 0.013). In the SCN analysis, network hubs in patients with fatigue worsening were detected in the putamen (p = 0.008 [left]; p = 0.007 [right]) and pons (p = 0.0001). INTERPRETATION: We unveiled predictive associations of specific subcortical gray matter volumes with fatigue in an early and initially untreated MS cohort. The colocalization of these subcortical structures with network hubs suggests an early role of these brain regions in terms of fatigue evolution. ANN NEUROL 2022;91:192-202.

Network alterations underlying anxiety symptoms in early multiple sclerosis.

BACKGROUND: Anxiety, often seen as comorbidity in multiple sclerosis (MS), is a frequent neuropsychiatric symptom and essentially affects the overall disease burden. Here, we aimed to decipher anxiety-related networks functionally connected to atrophied areas in patients suffering from MS. METHODS: Using 3-T MRI, anxiety-related atrophy maps were generated by correlating longitudinal cortical thinning with the severity of anxiety symptoms in MS patients. To determine brain regions functionally connected to these maps, we applied a technique termed "atrophy network mapping". Thereby, the anxiety-related atrophy maps were projected onto a large normative connectome (n = 1000) performing seed-based functional connectivity. Finally, an instructed threat paradigm was conducted with regard to neural excitability and effective connectivity, using transcranial magnetic stimulation combined with high-density electroencephalography. RESULTS: Thinning of the left dorsal prefrontal cortex was the only region that was associated with higher anxiety levels. Atrophy network mapping identified functional involvement of bilateral prefrontal cortex as well as amygdala and hippocampus. Structural equation modeling confirmed that the volumes of these brain regions were significant determinants that influence anxiety symptoms in MS. We additionally identified reduced information flow between the prefrontal cortex and the amygdala at rest, and pathologically increased excitability in the prefrontal cortex in MS patients as compared to controls. CONCLUSION: Anxiety-related prefrontal cortical atrophy in MS leads to a specific network alteration involving structures that resemble known neurobiological anxiety circuits. These findings elucidate the emergence of anxiety as part of the disease pathology and might ultimately enable targeted treatment approaches modulating brain networks in MS.

Altered grey matter integrity and network vulnerability relate to epilepsy occurrence in patients with multiple sclerosis.

BACKGROUND AND PURPOSE: The aim of this study was to investigate the relevance of compartmentalized grey matter (GM) pathology and network reorganization in multiple sclerosis (MS) patients with concomitant epilepsy. METHODS: From 3-T magnetic resonance imaging scans of 30 MS patients with epilepsy (MSE group; age 41 ± 15 years, 21 females, disease duration 8 ± 6 years, median Expanded Disability Status Scale [EDSS] score 3), 60 MS patients without epilepsy (MS group; age 41 ± 12 years, 35 females, disease duration 6 ± 4 years, EDSS score 2), and 60 healthy subjects (HS group; age 40 ± 13 years, 27 females) the regional volumes of GM lesions and of cortical, subcortical and hippocampal structures were quantified. Network topology and vulnerability were modelled within the graph theoretical framework. Receiver-operating characteristic (ROC) curve analysis was applied to assess the accuracy of GM pathology measures to discriminate between MSE and MS patients. RESULTS: Higher lesion volumes within the hippocampus, mesiotemporal cortex and amygdala were detected in the MSE compared to the MS group (all p < 0.05). The MSE group had lower cortical volumes mainly in temporal and parietal areas compared to the MS and HS groups (all p < 0.05). Lower hippocampal tail and presubiculum volumes were identified in both the MSE and MS groups compared to the HS group (all p < 0.05). Network topology in the MSE group was characterized by higher transitivity and assortativity, and higher vulnerability compared to the MS and HS groups (all p < 0.05). Hippocampal lesion volume yielded the highest accuracy (area under the ROC curve 0.80 [0.67-0.91]) in discriminating between MSE and MS patients. CONCLUSIONS: High lesion load, altered integrity of mesiotemporal GM structures, and network reorganization are associated with a greater propensity for epilepsy occurrence in people with MS.

Prognosis of a second clinical event from baseline MRI in patients with a CIS: a multicenter study using a machine learning approach.

PURPOSE: To predict the occurrence of a second clinical event in patients with a CIS suggestive of MS, from baseline magnetic resonance imaging (MRI), by means of a pattern recognition approach. METHODS: Two hundred sixty-six patients with a CIS were recruited from four participating centers. Over a follow-up of 3 years, 130 patients had a second clinical episode and 136 did not. Grey matter and white matter T1-hypointensities masks segmented from 3D T1-weighted images acquired on 3 T scanners were used as features for the classification approach. Differences between CIS that remained CIS and those that developed a second event were assessed at a global level and at a regional level, arranging the regions according to their contribution to the classification model. RESULTS: All classification metrics were around or even below 50% for both global and regional approaches. Accuracies did not change when T1-hypointensity maps were added to the model; just the specificity was increased up to 80%. Among the 30 regions with the largest contribution, 26 were grey matter and 4 were white matter regions. For grey matter, regions contributing showed either a larger or a smaller volume in the group of patients that remained CIS, compared to those with a second event. The volume of T1-hypointensities was always larger for the group that presented a second event. CONCLUSIONS: Prediction of a second clinical event in CIS patients from baseline MRI seems to present a highly heterogeneous pattern, leading to very low classification accuracies. Adding the T1-hypointensity maps does not seem to improve the accuracy of the classification model.

Normative vs. patient-specific brain connectivity in deep brain stimulation.

Brain connectivity profiles seeding from deep brain stimulation (DBS) electrodes have emerged as informative tools to estimate outcome variability across DBS patients. Given the limitations of acquiring and processing patient-specific diffusion-weighted imaging data, a number of studies have employed normative atlases of the human connectome. To date, it remains unclear whether patient-specific connectivity information would strengthen the accuracy of such analyses. Here, we compared similarities and differences between patient-specific, disease-matched and normative structural connectivity data and their ability to predict clinical improvement. Data from 33 patients suffering from Parkinson's Disease who underwent surgery at three different centers were retrospectively collected. Stimulation-dependent connectivity profiles seeding from active contacts were estimated using three modalities, namely patient-specific diffusion-MRI data, age- and disease-matched or normative group connectome data (acquired in healthy young subjects). Based on these profiles, models of optimal connectivity were calculated and used to estimate clinical improvement in out of sample data. All three modalities resulted in highly similar optimal connectivity profiles that could largely reproduce findings from prior research based on this present novel multi-center cohort. In a data-driven approach that estimated optimal whole-brain connectivity profiles, out-of-sample predictions of clinical improvements were calculated. Using either patient-specific connectivity (R = 0.43 at p = 0.001), an age- and disease-matched group connectome (R = 0.25, p = 0.048) and a normative connectome based on healthy/young subjects (R = 0.31 at p = 0.028), significant predictions could be made. Our results of patient-specific connectivity and normative connectomes lead to similar main conclusions about which brain areas are associated with clinical improvement. Still, although results were not significantly different, they hint at the fact that patient-specific connectivity may bear the potential of explaining slightly more variance than group connectomes. Furthermore, use of normative connectomes involves datasets with high signal-to-noise acquired on specialized MRI hardware, while clinical datasets as the ones used here may not exactly match their quality. Our findings support the role of DBS electrode connectivity profiles as a promising method to investigate DBS effects and to potentially guide DBS programming.

Characterizing the Molecular Architecture of Cortical Regions Associated with High Educational Attainment in Older Individuals.

Neuroimaging investigations have revealed interindividual variations in anatomy, metabolism, activity, and connectivity of specific cortical association areas through which years of education (YoE), as a common proxy of cognitive reserve, may operate in the face of age- or pathology-associated brain changes. However, the associated molecular properties of YoE-related brain regions and the biological pathways involved remain poorly understood. In the present study we first identified brain areas that showed an association between cortical thickness and YoE among 122 cognitively healthy older human individuals (87 female). We subsequently characterized molecular properties of these regions by studying brain-wide microarray measurements of regional gene expression. In accordance with previous studies, we observed that YoE were associated with higher cortical thickness in medial prefrontal, anterior cingulate, and orbitofrontal areas. Compared with the rest of the cortex, these regions exhibited a distinct gene expression profile characterized by relative upregulation of gene sets implicated in ionotropic and metabotropic neurotransmission as well as activation of immune response. Our genome-wide expression profile analysis of YoE-related brain regions points to distinct molecular pathways that may underlie a higher capacity for plastic changes in response to lifetime intellectual enrichment and potentially also a higher resilience to age-related pathologic brain changes.SIGNIFICANCE STATEMENT We combined a neuroimaging-based analysis with a transcriptome-wide gene expression approach to investigate the molecular-functional properties of cortical regions associated with educational attainment, as a commonly used proxy for cognitive reserve, in older individuals. The strongest association with education was observed in specific areas of the medial prefrontal cortex, and these areas exhibited a distinct gene expression profile characterized by relative upregulation of gene sets implicated in neurotransmission and immune responses. These findings complement previous neuroimaging studies in the field and point to novel biological pathways that may mediate the beneficial effects of high educational attainment on adaptability to cope with, or prevent, age-related brain changes. The identified genes and pathways now warrant further exploration in mechanistic studies.

Increased Neural Activity in Mesostriatal Regions after Prefrontal Transcranial Direct Current Stimulation and l-DOPA Administration.

Dopamine dysfunction is associated with a wide range of neuropsychiatric disorders commonly treated pharmacologically or invasively. Recent studies provide evidence for a nonpharmacological and noninvasive alternative that allows similar manipulation of the dopaminergic system: transcranial direct current stimulation (tDCS). In rodents, tDCS has been shown to increase neural activity in subcortical parts of the dopaminergic system, and recent studies in humans provide evidence that tDCS over prefrontal regions induces striatal dopamine release and affects reward-related behavior. Based on these findings, we used fMRI in healthy human participants and measured the fractional amplitude of low-frequency fluctuations to assess spontaneous neural activity strength in regions of the mesostriatal dopamine system before and after tDCS over prefrontal regions (n = 40, 22 females). In a second study, we examined the effect of a single dose of the dopamine precursor levodopa (l-DOPA) on mesostriatal fractional amplitude of low-frequency fluctuation values in male humans (n = 22) and compared the results between both studies. We found that prefrontal tDCS and l-DOPA both enhance neural activity in core regions of the dopaminergic system and show similar subcortical activation patterns. We furthermore assessed the spatial similarity of whole-brain statistical parametric maps, indicating tDCS- and l-DOPA-induced activation, and >100 neuronal receptor gene expression maps based on transcriptional data from the Allen Institute for Brain Science. In line with a specific activation of the dopaminergic system, we found that both interventions predominantly activated regions with high expression levels of the dopamine receptors D2 and D3.SIGNIFICANCE STATEMENT Studies in animals and humans provide evidence that transcranial direct current stimulation (tDCS) allows a manipulation of the dopaminergic system. Based on these findings, we used fMRI to assess changes in spontaneous neural activity strength in the human dopaminergic system after prefrontal tDCS compared with the administration of the dopamine precursor and standard anti-Parkinson drug levodopa (l-DOPA). We found that prefrontal tDCS and l-DOPA both enhance neural activity in core regions of the dopaminergic system and show similar subcortical activation patterns. Using whole-brain transcriptional data of >100 neuronal receptor genes, we found that both interventions specifically activated regions with high expression levels of the dopamine receptors D2 and D3.

Neuroimaging and electrophysiology meet invasive neurostimulation for causal interrogations and modulations of brain states.

Deep brain stimulation (DBS) has developed over the last twenty years into a highly effective evidenced-based treatment option for neuropsychiatric disorders. Moreover, it has become a fascinating tool to provide illustrative insights into the functioning of brain networks. New anatomical and pathophysiological models of DBS action have accelerated our understanding of neurological and psychiatric disorders and brain functioning. The description of the brain networks arose through the unique ability to illustrate long-range interactions between interconnected brain regions as derived from state-of-the-art neuroimaging (structural, diffusion, and functional MRI) and the opportunity to record local and large-scale brain activity at millisecond temporal resolution (microelectrode recordings, local field potential, electroencephalography, and magnetoencephalography). In the first part of this review, we describe how neuroimaging techniques have led to current understanding of DBS effects, by identifying and refining the DBS targets and illustrate the actual view on the relationships between electrode locations and clinical effects. One step further, we discuss how neuroimaging has shifted the view of localized DBS effects to a modulation of specific brain circuits, which has been possible from the combination of electrode location reconstructions with recently introduced network imaging methods. We highlight how these findings relate to clinical effects, thus postulating neuroimaging as a key factor to understand the mechanisms of DBS action on behavior and clinical effects. In the second part, we show how invasive electrophysiology techniques have been efficiently integrated into the DBS set-up to precisely localize the neuroanatomical targets of DBS based on distinct region-specific patterns of neural activity. Next, we show how multi-site electrophysiological recordings have granted a real-time window into the aberrant brain circuits within and beyond DBS targets to quantify and map the dynamic properties of rhythmic oscillations. We also discuss how DBS alters the transient synchrony states of oscillatory networks in temporal and spatial domains during resting, task-based and motion conditions, and how this modulation of brain states ultimately shapes the functional response. Finally, we show how a successful decoding and management of electrophysiological proxies (beta bursts, phase-amplitude coupling) of aberrant brain circuits was translated into adaptive DBS stimulation paradigms for a targeted and state-dependent invasive electrical neuromodulation.

Gray matter integrity predicts white matter network reorganization in multiple sclerosis.

Multiple sclerosis (MS) is a chronic inflammatory and neurodegenerative disease leading to gray matter atrophy and brain network reconfiguration as a response to increasing tissue damage. We evaluated whether white matter network reconfiguration appears subsequently to gray matter damage, or whether the gray matter degenerates following alterations in white matter networks. MRI data from 83 patients with clinically isolated syndrome and early relapsing-remitting MS were acquired at two time points with a follow-up after 1 year. White matter network integrity was assessed based on probabilistic tractography performed on diffusion-weighted data using graph theoretical analyses. We evaluated gray matter integrity by computing cortical thickness and deep gray matter volume in 94 regions at both time points. The thickness of middle temporal cortex and the volume of deep gray matter regions including thalamus, caudate, putamen, and brain stem showed significant atrophy between baseline and follow-up. White matter network dynamics, as defined by modularity and distance measure changes over time, were predicted by deep gray matter volume of the atrophying anatomical structures. Initial white matter network properties, on the other hand, did not predict atrophy. Furthermore, gray matter integrity at baseline significantly predicted physical disability at 1-year follow-up. In a sub-analysis, deep gray matter volume was significantly related to cognitive performance at baseline. Hence, we postulate that atrophy of deep gray matter structures drives the adaptation of white matter networks. Moreover, deep gray matter volumes are highly predictive for disability progression and cognitive performance.

Myelination- and immune-mediated MR-based brain network correlates.

BACKGROUND: Multiple sclerosis (MS) is an autoimmune disease of the central nervous system (CNS), characterized by inflammatory and neurodegenerative processes. Despite demyelination being a hallmark of the disease, how it relates to neurodegeneration has still not been completely unraveled, and research is still ongoing into how these processes can be tracked non-invasively. Magnetic resonance imaging (MRI) derived brain network characteristics, which closely mirror disease processes and relate to functional impairment, recently became important variables for characterizing immune-mediated neurodegeneration; however, their histopathological basis remains unclear. METHODS: In order to determine the MRI-derived correlates of myelin dynamics and to test if brain network characteristics derived from diffusion tensor imaging reflect microstructural tissue reorganization, we took advantage of the cuprizone model of general demyelination in mice and performed longitudinal histological and imaging analyses with behavioral tests. By introducing cuprizone into the diet, we induced targeted and consistent demyelination of oligodendrocytes, over a period of 5 weeks. Subsequent myelin synthesis was enabled by reintroduction of normal food. RESULTS: Using specific immune-histological markers, we demonstrated that 2 weeks of cuprizone diet induced a 52% reduction of myelin content in the corpus callosum (CC) and a 35% reduction in the neocortex. An extended cuprizone diet increased myelin loss in the CC, while remyelination commenced in the neocortex. These histologically determined dynamics were reflected by MRI measurements from diffusion tensor imaging. Demyelination was associated with decreased fractional anisotropy (FA) values and increased modularity and clustering at the network level. MRI-derived modularization of the brain network and FA reduction in key anatomical regions, including the hippocampus, thalamus, and analyzed cortical areas, were closely related to impaired memory function and anxiety-like behavior. CONCLUSION: Network-specific remyelination, shown by histology and MRI metrics, determined amelioration of functional performance and neuropsychiatric symptoms. Taken together, we illustrate the histological basis for the MRI-driven network responses to demyelination, where increased modularity leads to evolving damage and abnormal behavior in MS. Quantitative information about in vivo myelination processes is mirrored by diffusion-based imaging of microstructural integrity and network characteristics.