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A cortical parcellation technique accurately maps functional organization in individual brains. Functional networks mapped by this approach are highly reproducible and effectively capture individual variability. The algorithm performs well across different populations and data types and is validated by invasive cortical stimulation mapping in surgical patients. The capacity to identify the unique functional architecture of an individual's brain is a crucial step toward personalized medicine and understanding the neural basis of variation in human cognition and behavior. Here we developed a cortical parcellation approach to accurately map functional organization at the individual level using resting-state functional magnetic resonance imaging (fMRI). A population-based functional atlas and a map of inter-individual variability were employed to guide the iterative search for functional networks in individual subjects. Functional networks mapped by this approach were highly reproducible within subjects and effectively captured the variability across subjects, including individual differences in brain lateralization. The algorithm performed well across different subject populations and data types, including task fMRI data. The approach was then validated by invasive cortical stimulation mapping in surgical patients, suggesting potential for use in clinical applications.

Functional MRI (fMRI) studies have traditionally relied on intersubject normalization based on global brain morphology, which cannot establish proper functional correspondence between subjects due to substantial intersubject variability in functional organization. Here, we reliably identified a set of discrete, homologous functional regions in individuals to improve intersubject alignment of fMRI data. These functional regions demonstrated marked intersubject variability in size, position, and connectivity. We found that previously reported intersubject variability in functional connectivity maps could be partially explained by variability in size and position of the functional regions. Importantly, individual differences in network topography are associated with individual differences in task-evoked activations, suggesting that these individually specified regions may serve as the \"localizer\" to improve the alignment of task-fMRI data. We demonstrated that aligning task-fMRI data using the regions derived from resting state fMRI may lead to increased statistical power of task-fMRI analyses. In addition, resting state functional connectivity among these homologous regions is able to capture the idiosyncrasies of subjects and better predict fluid intelligence (gF) than connectivity measures derived from group-level brain atlases. Critically, we showed that not only the connectivity but also the size and position of functional regions are related to human behavior. Collectively, these findings suggest that identifying homologous functional regions across individuals can benefit a wide range of studies in the investigation of connectivity, task activation, and brain-behavior associations.

•Correlation and distance metrics are appropriate to quantify functional connectivity.•Partial correlation performs worse in detecting neural decline.•But: best metric depends on scanning sequence, regions of interest, and subjects investigated.•Choice of functional connectivity metric is study-specific.•Brain perfusion measured by PCASL is a robust neural correlate of cognitive decline. Functional connectivity in the context of functional magnetic resonance imaging is typically quantified by Pearson´s or partial correlation between regional time series of the blood oxygenation level dependent signal. However, a recent interdisciplinary methodological work proposes >230 different metrics to measure similarity between different types of time series. Hence, we systematically evaluated how the results of typical research approaches in functional neuroimaging vary depending on the functional connectivity metric of choice. We further explored which metrics most accurately detect presumed reductions in connectivity related to age and malignant brain tumors, aiming to initiate a debate on the best approaches for assessing brain connectivity in functional neuroimaging research. We addressed both research questions using four independent neuroimaging datasets, comprising multimodal data from a total of 1187 individuals. We analyzed resting-state functional sequences to calculate functional connectivity using 20 representative metrics from four distinct mathematical domains. We further used T1- and T2-weighted images to compute regional brain volumes, diffusion-weighted imaging data to build structural connectomes, and pseudo-continuous arterial spin labeling to measure regional brain perfusion. First, our findings demonstrate that the results of typical functional neuroimaging approaches differ fundamentally depending on the functional connectivity metric of choice. Second, we show that correlational and distance metrics are most appropriate to cover reductions in connectivity linked to aging. In this context, partial correlation performs worse than other correlational metrics. Third, our findings suggest that the FC metric of choice depends on the utilized scanning parameters, the regions of interest, and the individual investigated. Lastly, beyond the major objective of this study, we provide evidence in favor of brain perfusion measured via pseudo-continuous arterial spin labeling as a robust neural entity mirroring age-related neural and cognitive decline. Our empirical evaluation supports a recent theoretical functional connectivity framework. Future functional imaging studies need to comprehensively define the study-specific theoretical property of interest, the methodological property to assess the theoretical property, and the confounding property that may bias the conclusions.

Functional connectivity (FC) is known to be individually unique and to reflect cognitive variability. Although FC can serve as a valuable correlate and potential predictor of (patho-) physiological nervous function in high-risk constellations, such as preterm birth, templates for individualized FC analysis are lacking, and knowledge about the capacity of the premature brain to develop FC variability is limited. In a cohort of prospectively recruited, preterm-born infants undergoing magnetic resonance imaging close to term-equivalent age, we show that the overall pattern could be reliably detected with a broad range of interindividual FC variability in regions of higher-order cognitive functions (e.g., association cortices) and less interindividual variability in unimodal regions (e.g., visual and motor cortices). However, when comparing the preterm and adult brains, some brain regions showed a marked shift in variability toward adulthood. This shift toward greater variability was strongest in cognitive networks like the attention and frontoparietal networks and could be partially predicted by developmental cortical expansion. Furthermore, FC variability was reflected by brain tissue characteristics indicating cortical maturation. Brain regions with high functional variability (e.g., the inferior frontal gyrus and temporoparietal junction) displayed lower cortical maturation at birth compared with somatosensory cortices. In conclusion, the overall pattern of interindividual variability in FC is already present preterm; however, some brain regions show increased variability toward adulthood, identifying characteristic patterns, such as in cognitive networks. These changes are related to postnatal cortical expansion and maturation, allowing for environmental and developmental factors to translate into marked individual differences in FC.

ObjectivesPrior studies relating body mass index (BMI) to brain volumes suggest an overall inverse association. However, BMI might not be an ideal marker, as it disregards different fat compartments, which carry different metabolic risks. Therefore, we analyzed MR-based fat depots and their association with gray matter (GM) volumes of brain structures, which show volumetric changes in neurodegenerative diseases.MethodsWarp-based automated brain segmentation of 3D FLAIR sequences was obtained in a population-based study cohort. Associations of temporal lobe, cingulate gyrus, and hippocampus GM volume with BMI and MR-based quantification of visceral adipose tissue (VAT), as well as hepatic and pancreatic proton density fat fraction (PDFFhepatic and PDFFpanc, respectively), were assessed by linear regression.ResultsIn a sample of 152 women (age 56.2 ± 9.0 years) and 199 men (age 56.1 ± 9.1 years), we observed a significant inverse association of PDFFhepatic and cingulate gyrus volume (p < 0.05) as well as of PDFFhepatic and hippocampus volume (p < 0.05), when adjusting for age and sex. This inverse association was further enhanced for cingulate gyrus volume after additionally adjusting for hypertension, smoking, BMI, LDL, and total cholesterol (p < 0.01) and also alcohol (p < 0.01). No significant association was observed between PDFFhepatic and temporal lobe and between temporal lobe, cingulate gyrus, or hippocampus volume and BMI, VAT, and PDFFpanc.ConclusionsWe observed a significant inverse, independent association of cingulate gyrus and hippocampus GM volume with hepatic fat, but not with other obesity measures. Increased hepatic fat could therefore serve as a marker of high-risk fat distribution.Key Points• Obesity is associated with neurodegenerative processes.• In a population-based study cohort, hepatic fat was superior to BMI and visceral and pancreatic fat as a risk biomarker for decreased brain volume of cingulate gyrus and hippocampus.• Increased hepatic fat could serve as a marker of high-risk fat distribution.

To identify the most important parameters associated with cerebral white matter hyperintensities (WMH), in consideration of potential collinearity, we used a data-driven machine-learning approach. We analysed two independent cohorts (KORA and SHIP). WMH volumes were derived from cMRI-images (FLAIR). 90 (KORA) and 34 (SHIP) potential determinants of WMH including measures of diabetes, blood-pressure, medication-intake, sociodemographics, life-style factors, somatic/depressive-symptoms and sleep were collected. Elastic net regression was used to identify relevant predictor covariates associated with WMH volume. The ten most frequently selected variables in KORA were subsequently examined for robustness in SHIP. The final KORA sample consisted of 370 participants (58% male; age 55.7 ± 9.1 years), the SHIP sample comprised 854 participants (38% male; age 53.9 ± 9.3 years). The most often selected and highly replicable parameters associated with WMH volume were in descending order age, hypertension, components of the social environment (i.e. widowed, living alone) and prediabetes. A systematic machine-learning based analysis of two independent, population-based cohorts showed, that besides age and hypertension, prediabetes and components of the social environment might play important roles in the development of WMH. Our results enable personal risk assessment for the development of WMH and inform prevention strategies tailored to the individual patient.

Glioblastoma might have widespread effects on the neural organization and cognitive function, and even focal lesions may be associated with distributed functional alterations. However, functional changes do not necessarily follow obvious anatomical patterns and the current understanding of this interrelation is limited. In this study, we used resting-state functional magnetic resonance imaging to evaluate changes in global functional connectivity patterns in 15 patients with glioblastoma. For six patients we followed longitudinal trajectories of their functional connectome and structural tumour evolution using bi-monthly follow-up scans throughout treatment and disease progression. In all patients, unilateral tumour lesions were associated with inter-hemispherically symmetric network alterations, and functional proximity of tumour location was stronger linked to distributed network deterioration than anatomical distance. In the longitudinal subcohort of six patients, we observed patterns of network alterations with initial transient deterioration followed by recovery at first follow-up, and local network deterioration to precede structural tumour recurrence by two months. In summary, the impact of focal glioblastoma lesions on the functional connectome is global and linked to functional proximity rather than anatomical distance to tumour regions. Our findings further suggest a relevance for functional network trajectories as a possible means supporting early detection of tumour recurrence.

Objectives Reactive gliosis is a common pathological hallmark of CNS pathology resulting from neurodegeneration and neuroinflammation. In this study we investigate the capability of a novel monoamine oxidase B (MAO-B) PET ligand to monitor reactive astrogliosis in a transgenic mouse model of Alzheimer`s disease (AD). Furthermore, we performed a pilot study in patients with a range of neurodegenerative and neuroinflammatory conditions. Methods A cross-sectional cohort of 24 transgenic (PS2APP) and 25 wild-type mice (age range: 4.3–21.0 months) underwent 60 min dynamic [ 18 F]fluorodeprenyl-D2 ([ 18 F]F-DED), static 18 kDa translocator protein (TSPO, [ 18 F]GE-180) and β-amyloid ([ 18 F]florbetaben) PET imaging. Quantification was performed via image derived input function (IDIF, cardiac input), simplified non-invasive reference tissue modelling (SRTM2, DVR) and late-phase standardized uptake value ratios (SUVr). Immunohistochemical (IHC) analyses of glial fibrillary acidic protein (GFAP) and MAO-B were performed to validate PET imaging by gold standard assessments. Patients belonging to the Alzheimer’s disease continuum (AD, n  = 2), Parkinson’s disease (PD, n  = 2), multiple system atrophy (MSA, n  = 2), autoimmune encephalitis ( n  = 1), oligodendroglioma ( n  = 1) and one healthy control underwent 60 min dynamic [ 18 F]F-DED PET and the data were analyzed using equivalent quantification strategies. Results We selected the cerebellum as a pseudo-reference region based on the immunohistochemical comparison of age-matched PS2APP and WT mice. Subsequent PET imaging revealed that PS2APP mice showed elevated hippocampal and thalamic [ 18 F]F-DED DVR when compared to age-matched WT mice at 5 months (thalamus: + 4.3%; p  = 0.048), 13 months (hippocampus: + 7.6%, p  = 0.022) and 19 months (hippocampus: + 12.3%, p  < 0.0001; thalamus: + 15.2%, p  < 0.0001). Specific [ 18 F]F-DED DVR increases of PS2APP mice occurred earlier when compared to signal alterations in TSPO and β-amyloid PET and [ 18 F]F-DED DVR correlated with quantitative immunohistochemistry (hippocampus: R  = 0.720, p  < 0.001; thalamus: R  = 0.727, p  = 0.002). Preliminary experience in patients showed [ 18 F]F-DED V T and SUVr patterns, matching the expected topology of reactive astrogliosis in neurodegenerative (MSA) and neuroinflammatory conditions, whereas the patient with oligodendroglioma and the healthy control indicated [ 18 F]F-DED binding following the known physiological MAO-B expression in brain. Conclusions [ 18 F]F-DED PET imaging is a promising approach to assess reactive astrogliosis in AD mouse models and patients with neurological diseases.

Transcranial Direct Current Stimulation (tDCS) of the prefrontal cortex (PFC) can be used for probing functional brain connectivity and meets general interest as novel therapeutic intervention in psychiatric and neurological disorders. Along with a more extensive use, it is important to understand the interplay between neural systems and stimulation protocols requiring basic methodological work. Here, we examined the test-retest (TRT) characteristics of tDCS-induced modulations in resting-state functional-connectivity MRI (RS fcMRI). Twenty healthy subjects received 20minutes of either active or sham tDCS of the dorsolateral PFC (2mA, anode over F3 and cathode over F4, international 10–20 system), preceded and ensued by a RS fcMRI (10minutes each). All subject underwent three tDCS sessions with one-week intervals in between. Effects of tDCS on RS fcMRI were determined at an individual as well as at a group level using both ROI-based and independent-component analyses (ICA). To evaluate the TRT reliability of individual active-tDCS and sham effects on RS fcMRI, voxel-wise intra-class correlation coefficients (ICC) of post-tDCS maps between testing sessions were calculated. For both approaches, results revealed low reliability of RS fcMRI after active tDCS (ICC(2,1) = −0.09 – 0.16). Reliability of RS fcMRI (baselines only) was low to moderate for ROI-derived (ICC(2,1) = 0.13 – 0.50) and low for ICA-derived connectivity (ICC(2,1) = 0.19 – 0.34). Thus, for ROI-based analyses, the distribution of voxel-wise ICC was shifted to lower TRT reliability after active, but not after sham tDCS, for which the distribution was similar to baseline. The intra-individual variation observed here resembles variability of tDCS effects in motor regions and may be one reason why in this study robust tDCS effects at a group level were missing. The data can be used for appropriately designing large scale studies investigating methodological issues such as sources of variability and localisation of tDCS effects. •Prefrontal non-invasive brain stimulation targeting specific brain circuits has the potential to be applied in therapeutic settings but reliability, validity and generalisability have to be evaluated.•This is the first study investigating the test-retest reliability of prefrontal tDCS-induced resting-state functional-connectivity (RS fcMRI) modulations.•Analyses of individual RS-fcMRI responses to active tDCS across three single sessions revealed no to low reliability, whereas reliability of RS-fcMRI baselines and RS-fcMRI responses to sham tDCS was low to moderate.•Our pilot data can be used to plan future imaging studies investigating rs-fcMRI effects of prefrontal tDCS.

Abstract Background and Hypothesis Aerobic exercise interventions in people with schizophrenia have been demonstrated to improve clinical outcomes, but findings regarding the underlying neural mechanisms are limited and mainly focus on the hippocampal formation. Therefore, we conducted a global exploratory analysis of structural and functional neural adaptations after exercise and explored their clinical implications. Study Design In this randomized controlled trial, structural and functional MRI data were available for 91 patients with schizophrenia who performed either aerobic exercise on a bicycle ergometer or underwent a flexibility, strengthening, and balance training as control group. We analyzed clinical and neuroimaging data before and after 6 months of regular exercise. Bayesian linear mixed models and Bayesian logistic regressions were calculated to evaluate effects of exercise on multiple neural outcomes and their potential clinical relevance. Study Results Our results indicated that aerobic exercise in people with schizophrenia led to structural and functional adaptations mainly within the default-mode network, the cortico-striato-pallido-thalamo-cortical loop, and the cerebello-thalamo-cortical pathway. We further observed that volume increases in the right posterior cingulate gyrus as a central node of the default-mode network were linked to improvements in disorder severity. Conclusions These exploratory findings suggest a positive impact of aerobic exercise on 3 cerebral networks that are involved in the pathophysiology of schizophrenia. Clinical Trials Registration The underlying study of this manuscript was registered in the International Clinical Trials Database, ClinicalTrials.gov (NCT number: NCT03466112, https://clinicaltrials.gov/ct2/show/NCT03466112?term=NCT03466112&draw=2&rank=1) and in the German Clinical Trials Register (DRKS-ID: DRKS00009804).