We propose diagnostic criteria for Susac syndrome that may help both experts and physicians not familiar with Susac syndrome to make a correct diagnosis and to prevent delayed treatment initiation.

BackgroundThe human inferior frontal junction area (IFJ) is critically involved in three main component processes of cognitive control (working memory, task switching and inhibitory control). As it overlaps with several areas in established anatomical labeling schemes, it is considered to be underreported as a functionally distinct location in the neuroimaging literature. While recent studies explicitly focused on the IFJ's anatomical organization and functional role as a single brain area, it is usually not explicitly denominated in studies on cognitive networks. However based on few analyses in small datasets constrained by specific a priori assumptions on its functional specialization, the IFJ has been postulated to be part of a cognitive control network. Goal of this meta-analysis was to establish the IFJ’s connectivity profile on a high formal level of evidence by aggregating published implicit knowledge about its co-activations. We applied meta-analytical connectivity modeling (MACM) based on the activation likelihood estimation (ALE) method without specific assumptions regarding functional specialization on 180 (reporting left IFJ activity) and 131 (right IFJ) published functional neuroimaging experiments derived from the BrainMap database. This method is based on coordinates in stereotaxic space, not on anatomical descriptors.ResultsThe IFJ is significantly co-activated with areas in the dorsolateral and ventrolateral prefrontal cortex, anterior insula, medial frontal gyrus / pre-SMA, posterior parietal cortex, occipitotemporal junction / cerebellum, thalamus and putamen as well as language and motor areas. Results are corroborated by an independent resting-state fMRI analysis.ConclusionsThese results support the assumption that the IFJ is part of a previously described cognitive control network. They also highlight the involvement of subcortical structures in this system. A direct line is drawn from works on the functional significance of brain activity located at the IFJ and its anatomical definition to published results related to distributed cognitive brain systems. The IFJ is therefore introduced as a convenient starting point to investigate the cognitive control network in further studies.

Information derived from functional magnetic resonance imaging (fMRI) during wakeful rest has been introduced as a candidate diagnostic biomarker in unipolar major depressive disorder (MDD). Multiple reports of resting state fMRI in MDD describe group effects. Such prior knowledge can be adopted to pre-select potentially discriminating features for diagnostic classification models with the aim to improve diagnostic accuracy. Purpose of this analysis was to consolidate spatial information about alterations of spontaneous brain activity in MDD, primarily to serve as feature selection for multivariate pattern analysis techniques (MVPA). Thirty two studies were included in final analyses. Coordinates extracted from the original reports were assigned to two categories based on directionality of findings. Meta-analyses were calculated using the non-additive activation likelihood estimation approach with coordinates organized by subject group to account for non-independent samples. Converging evidence revealed a distributed pattern of brain regions with increased or decreased spontaneous activity in MDD. The most distinct finding was hyperactivity/hyperconnectivity presumably reflecting the interaction of cortical midline structures (posterior default mode network components including the precuneus and neighboring posterior cingulate cortices associated with self-referential processing and the subgenual anterior cingulate and neighboring medial frontal cortices) with lateral prefrontal areas related to externally-directed cognition. Other areas of hyperactivity/hyperconnectivity include the left lateral parietal cortex, right hippocampus and right cerebellum whereas hypoactivity/hypoconnectivity was observed mainly in the left temporal cortex, the insula, precuneus, superior frontal gyrus, lentiform nucleus and thalamus. Results are made available in two different data formats to be used as spatial hypotheses in future studies, particularly for diagnostic classification by MVPA.

SUMMARY:There has been a recent upsurge of reports about applications of pattern-recognition techniques from the field of machine learning to functional MR imaging data as a diagnostic tool for systemic brain disease or psychiatric disorders. Entities studied include depression, schizophrenia, attention deficit hyperactivity disorder, and neurodegenerative disorders like Alzheimer dementia. We review these recent studies which-despite the optimism from some articles-predominantly constitute explorative efforts at the proof-ofconcept level. There is some evidence that, in particular, support vector machines seem to be promising. However, the field is still far from real clinical application, and much work has to be done regarding data preprocessing, model optimization, and validation. Reporting standards are proposed to facilitate future meta-analyses or systematic reviews. ABBREVIATIONS:ADHD ϭ attention deficit hyperactivity disorder; CV ϭ cross-validation; LDA ϭ linear discriminant analysis; MVPA ϭ multivariate pattern

Depression has been associated with various alterations in magnetic resonance imaging (MRI) derived resting-state functional connectivity. Recently, homotopic connectivity, defined as functional connectivity between homotopic regions across hemispheres, has been reported to be reduced in patients with major depressive disorder (MDD). However, little is known about structural factors underlying alterations of homotopic connectivity, which would contribute to the understanding of the altered neurophysiological architecture in patients with MDD. We compared 368 patients with MDD and 461 never-depressed controls regarding voxel-mirrored homotopic connectivity (VMHC) and potential underlying mechanisms such as the structural connectivity of the corpus callosum, measured by DTI-derived fractional anisotropy (FA), and left-right symmetries in homotopic gray matter volumes. Compared to controls, patients with MDD exhibited reduced VMHC in the cuneus, putamen, superior temporal gyrus, insula, and precuneus. Within these regions, no differences in left-right symmetries in homotopic gray matter volumes were evident across cohorts. FA of the corpus callosum correlated with VMHC in the entire sample. However, patients with MDD and controls did not differ with regard to callosal FA. The findings indicate that MDD is associated with a loss of interhemispheric synchrony in regions known to be implicated in self-referential and reward processing. They also suggest that additional mechanisms are implicated in altered homotopic connectivity of patients with MDD, other than direct callosal fiber pathways or asymmetries in homotopic gray matter volumes.

Neither a generally slower task performance nor distinctively altered functioning of brain networks involved in a task representing a subset of dopamine-dependent executive functions could be proven. Decreased accuracy and inconsistent findings in posterior areas necessitate further study of frontal-lobe functioning in PKU patients in larger study samples.

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Individual differences in sensitivity to pain are large and have clinical and scientific importance. Although heavily influenced by situational factors, they also relate to genetic factors and psychological traits, and are reflected by differences in functional activation in pain-related brain regions. Here, we used voxel-based morphometry to investigate if individual pain sensitivity is related to local gray matter volumes. Pain sensitivity was determined using (1) index finger pressure pain thresholds (PPTs) and (2) pain intensity ratings of imagined painful situations as assessed by the Pain Sensitivity Questionnaire (PSQ) in 501 population-based subjects participating in the BiDirect Study. Pain Sensitivity Questionnaire scores were positively associated with gray matter in 2 symmetrical clusters, with a focus on the parahippocampal gyrus, extending to the hippocampus, fusiform gyrus, BA19, putamen, and insula (P < 0.05 corrected), but the effect was small (R = 0.045-0.039). No negative associations with the PSQ and no associations with the PPT reached significance. Parahippocampal activation during pain and altered parahippocampal gray matter in chronic pain have been reported, which would be consistent with positive associations with PSQ scores. Alternatively, associations of PSQ scores with the parahippocampal and fusiform gray matter could relate to the visual imagination of painful situations required by the PSQ, not to pain sensitivity itself. Regarding PPTs, the present data obtained in a large sample strongly suggest an absence of associations of this parameter with gray matter volume. In conclusion, the present results argue against a strong association between pain sensitivity and local gray matter volumes.