It is unknown whether brain changes occur prior to onset of schizophrenia or after it develops. Prospective familial high risk studies provide a good method to investigate this. In the Edinburgh High Risk Study, structural MRI scans of 150 young individuals at familial high risk of schizophrenia, 34 patients with first-episode schizophrenia and 36 matched controls were obtained. Of the high risk participants with scans suitable for analysis, 17 developed schizophrenia after the scans were taken, whilst 57 experienced isolated or sub-clinical psychotic symptoms, and 70 remained well. We used Freesurfer to extract volumetric measurements of the hippocampus, amygdala and nucleus accumbens with the aim of assessing whether any alterations found were present in all those at high risk, or selectively in the high risk cohort based on future clinical outcome, or only in those experiencing their first-episode of psychosis. We found no significant differences in any examined regions between controls and those at high risk, or between those at high risk who later developed schizophrenia and those who remained well. However, patients with first-episode schizophrenia demonstrated significant volumetric reductions in the bilateral hippocampus, left amygdala, and right nucleus accumbens compared to high risk individuals and healthy controls, which were not significantly associated with the intake of anti-psychotic medication or duration of illness. We found that patients had significantly smaller left amygdalae and bilateral hippocampus compared to HR[ill]. Our findings suggest that volumetric reductions of the hippocampus, amygdala and nucleus accumbens occur early in the first-episode of psychosis. The apparent absence of high risk versus control differences we found using Freesurfer is at odds with our previous studies conducted on the same sample, and possible methodological reasons for these apparent discrepancies are discussed.

In this study, we aimed to understand how whole-brain neural networks compute sensory information integration based on the olfactory and visual system. Task-related functional magnetic resonance imaging (fMRI) data was obtained during unimodal and bimodal sensory stimulation. Based on the identification of multisensory integration processing (MIP) specific hub-like network nodes analyzed with network-based statistics using region-of-interest based connectivity matrices, we conclude the following brain areas to be important for processing the presented bimodal sensory information: right precuneus connected contralaterally to the supramarginal gyrus for memory-related imagery and phonology retrieval, and the left middle occipital gyrus connected ipsilaterally to the inferior frontal gyrus via the inferior fronto-occipital fasciculus including functional aspects of working memory. Applied graph theory for quantification of the resulting complex network topologies indicates a significantly increased global efficiency and clustering coefficient in networks including aspects of MIP reflecting a simultaneous better integration and segregation. Graph theoretical analysis of positive and negative network correlations allowing for inferences about excitatory and inhibitory network architectures revealed-not significant, but very consistent-that MIP-specific neural networks are dominated by inhibitory relationships between brain regions involved in stimulus processing.

Functional MRI (fMRI) studies have reported altered integrity of large-scale neurocognitive networks (NCNs) in dementing disorders. However, findings on the specificity of these alterations in patients with Alzheimer disease (AD) and behavioral-variant frontotemporal dementia (bvFTD) are still limited. Recently, NCNs have been successfully captured using PET with 18 F-FDG. Methods: Network integrity was measured in 72 individuals (38 male) with mild AD or bvFTD, and in healthy controls, using a simultaneous restingstate fMRI and 18 F-FDG PET. Indices of network integrity were calculated for each subject, network, and imaging modality. Results: In either modality, independent-component analysis revealed 4 major NCNs: anterior default-mode network (DMN), posterior DMN, salience network, and right central executive network (CEN). In fMRI data, the integrity of the posterior DMN was found to be significantly reduced in both patient groups relative to controls. In the AD group the anterior DMN and CEN appeared to be additionally affected. In PET data, only the integrity of the posterior DMN in patients with AD was reduced, whereas 3 remaining networks appeared to be affected only in patients with bvFTD. In a logistic regression analysis, the integrity of the anterior DMN as measured with PET alone accurately differentiated between the patient groups. A correlation between indices of 2 imaging modalities was low overall. Conclusion: FMRI and 18 F-FDG PET capture partly different aspects of network integrity. A higher disease specificity for NCNs as derived from PET data supports metabolic connectivity imaging as a promising diagnostic tool.

Neural correlates of working memory (WM) training remain a matter of debate, especially in older adults. We used functional magnetic resonance imaging (fMRI) together with an n-back task to measure brain plasticity in healthy middle-aged adults following an 8-week adaptive online verbal WM training. Participants performed 32 sessions of this training on their personal computers. In addition, we assessed direct effects of the training by applying a verbal WM task before and after the training. Participants (mean age 55.85 ± 4.24 years) were pseudo-randomly assigned to the experimental group (n = 30) or an active control group (n = 27). Training resulted in an activity decrease in regions known to be involved in verbal WM (i.e., fronto-parieto-cerebellar circuitry and subcortical regions), indicating that the brain became potentially more efficient after the training. These activation decreases were associated with a significant performance improvement in the n-back task inside the scanner reflecting considerable practice effects. In addition, there were training-associated direct effects in the additional, external verbal WM task (i.e., HAWIE-R digit span forward task), and indicating that the training generally improved performance in this cognitive domain. These results led us to conclude that even at advanced age cognitive training can improve WM capacity and increase neural efficiency in specific regions or networks.

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Purpose Inter-subject covariance of regional 18F-fluorodeoxyglucose (FDG) PET measures (FDGcov) as proxy of brain connectivity has been gaining an increasing acceptance in the community. Yet, it is still unclear to what extent FDGcov is underlied by actual structural connectivity via white matter fiber tracts. In this study, we quantified the degree of spatial overlap between FDGcov and structural connectivity networks. Methods We retrospectively analyzed neuroimaging data from 303 subjects, both patients with suspected neurodegenerative disorders and healthy individuals. For each subject, structural magnetic resonance, diffusion tensor imaging, and FDG-PET data were available. The images were spatially normalized to a standard space and segmented into 62 anatomical regions using a probabilistic atlas. Sparse inverse covariance estimation was employed to estimate FDGcov. Structural connectivity was measured by streamline tractography through fiber assignment by continuous tracking. Results For the whole brain, 55% of detected connections were found to be convergent, i.e., present in both FDGcov and structural networks. This metric for random networks was significantly lower, i.e., 12%. Convergent were 80% of intralobe connections and only 30% of interhemispheric interlobe connections. Conclusion Structural connectivity via white matter fiber tracts is a relevant substrate of FDGcov, underlying around a half of connections at the whole brain level. Short-range white matter tracts appear to be a major substrate of intralobe FDGcov connections.

Recently, Jamadar et al. (2021, Metabolic and hemodynamic resting-state connectivity of the human brain: a high-temporal resolution simultaneous BOLD-fMRI and FDG-fPET multimodality study. Cereb Cortex. 31(6), 2855–2867) compared the patterns of brain connectivity or covariance as obtained from 3 neuroimaging measures: 1) functional connectivity estimated from temporal correlations in the functional magnetic resonance imaging blood oxygen level-dependent signal, metabolic connectivity estimated, 2) from temporal correlations in 16-s frames of dynamic [18F]-fluorodeoxyglucose-positron emission tomography (FDG-PET), which they designate as functional FDG-PET (fPET), and 3) from intersubject correlations in static FDG-PET images (sPET). Here, we discuss a number of fundamental issues raised by the Jamadar study. These include the choice of terminology, the interpretation of cross-modal findings, the issue of group- to single-subject level inferences, and the meaning of metabolic connectivity as a biomarker. We applaud the methodological approach taken by the authors, but wish to present an alternative perspective on their findings. In particular, we argue that sPET and fPET can both provide valuable information about brain connectivity. Certainly, resolving this conundrum calls for further experimental and theoretical efforts to advance the developing framework of PET-based brain connectivity indices.

Despite growing interest in cognitive interventions from academia and industry, it remains unclear if working memory (WM) training, one of the most popular cognitive interventions, produces transfer effects. Transfer effects are training-induced gains in performance in untrained cognitive tasks, while practice effects are improvements in trained task. The goal of this study was to evaluate potential transfer effects by comprehensive cognitive testing and neuroimaging. In this prospective, randomized-controlled, and single-blind study, we administered an 8-week n-back training to 55 healthy middle-aged (50–64 years) participants. State-of-the-art multimodal neuroimaging was used to examine potential anatomic and functional changes. Relative to control subjects, who performed non-adaptive WM training, no near or far transfer effects were detected in experimental subjects, who performed adaptive WM training. Equivalently, no training-related changes were observed in white matter integrity, amplitude of low frequency fluctuations, glucose metabolism, functional and metabolic connectivity. Exploratory within-group comparisons revealed some gains in transfer tasks, which, however, cannot be attributed to an increased WM capacity. In conclusion, WM training produces transfer effects neither at the cognitive level nor in terms of neural structure or function. These results speak against a common view that training-related gains reflect an increase in underlying WM capacity. Instead, the presently observed practice effects may be a result of optimized task processing strategies, which do not necessarily engage neural plasticity.