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Previous studies have shown that machine-learning (ML) algorithms can “predict” sex based on brain anatomical/ functional features. The high classification accuracy achieved by ML algorithms is often interpreted as revealing large differences between the brains of males and females and as confirming the existence of “male/female brains”. However, classification and estimation are quite different concepts, and using classification metrics as surrogate estimates of between-group differences results in major statistical and interpretative distortions. The present study illustrates these distortions and provides a novel and detailed assessment of multivariate sex differences in gray matter volume (GMVOL) that does not rely on classification metrics. Moreover, modeling and clustering techniques and analyses of similarities (ANOSIM) were used to identify the brain areas that contribute the most to these multivariate differences, and to empirically assess whether they assemble into two sex-typical profiles. Results revealed that multivariate sex differences in GMVOL: 1) are “large” if not adjusted for total intracranial volume (TIV) variation, but “small” when controlling for this variable; 2) differ in size between individuals and also depends on the ML algorithm used for their calculation 3) do not stem from two sex-typical profiles, and so describing them in terms of “male/female brains” is misleading.

The adhesio interthalamica (AI) is a small midline brain structure that connects the left and right thalamus. According to in vivo data, between 2.3–22.3% of the general population lack the AI, and the question of whether this absence is more prevalent in males than in females is a matter of debate. Despite the existence of these demographic figures, it remains unclear how this distinctive feature affects healthy people, or what specific anatomic profile is related to the presence or absence of the AI. The aim of this study was to investigate whole brain gray matter (GM) volumetric differences depending on the presence or absence of the AI. A total of 240 healthy adult volunteers completed one MRI scanning session. After the AI assessment, the data from 110 participants were included in the final sample, of which 12.9% of the participants (n = 31) presented complete AI absence vs. 32.9% of participants (n = 79) who presented complete AI presence. Then, whole-brain group comparison analysis revealed that the absent AI brain, compared to the present AI brain, was associated with lower GM volume in the premotor cortex, inferior frontal gyrus, and anterior temporal cortex. Interestingly, neuroscience research has linked emotional and cognitive control brain processing to the latter two regions. The importance of these findings lies in providing a neuroanatomical profile for the absent AI brain in healthy human adults.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

The present study aimed to investigate altered grey matter (GM) and functional connectivity (FC) in deep subcortical areas, like the thalamus and basal ganglia, and their relationship with cognitive impairment in multiple sclerosis (MS). Thirty-six patients were neuropsychologically assessed, classified as cognitive preserved (CP) and cognitive impairment (CI), and were compared with 18 healthy controls (HC). GM atrophy and FC were observed in 10 predefined functional areas of the thalamus and in six of basal ganglia. GM atrophy was prominent in the basal ganglia in CI patients compared to CP MS patients. Increased FC was observed between the right caudate and the bilateral orbitofrontal cortex in CI vs. CP patients. The discriminant and correlation analyses revealed that the enhanced FC observed between the right caudate and the orbitofrontal cortex was closely associated with cognitive impairment in MS patients. In conclusion, reduced GM volume and enhanced fronto-basal ganglia connectivity are related to cognition in MS patients.

Working memory training causes functional adaptations in the brain, which include changes in activation and functional connectivity that remain stable over time. Few studies have investigated gray matter (GM) changes after working memory training, and they have produced heterogeneous results without clarifying the stable effects of training. The present study was designed to test for sustained and transient anatomic changes after only 200 min of working memory training. The voxel-based morphometry technique was used in order to investigate the GM changes produced by a brief single n-back training, immediately and 5 weeks after finishing it. The sample was composed by 59 human participants who underwent MRI scanning and were assigned to either a training group or a passive control group. Results showed sustained GM volume enlargement in the right superior parietal cortex and a transient GM decrease in the right putamen. The brain adaptation in the right superior parietal cortex was stronger in individuals who showed greater improvements in performance. The results provide further evidence that a brief working memory training is able to produce brain plasticity in structures related to the trained task. This work was supported by grants from Universitat Jaume I (P1•1B2013-63) and Ministerio de Economia y Competitividad (PSI2013-47504-R and PSI2016-78805-R). Authors J. Adrián-Ventura, E. Villar-Rodriguez, L. Marin-Marin and N. Aguirre were supported by a pre-doctoral graduate program grant (National FPU to J.A-V, L.M-M, E.V-R and N.A). In addition, this work was supported by a post-doctoral graduate program grant to V. Costumero, M-Á. Palomar-García and A. Miró-Padilla (Juan de la Cierva to V.C and postdoc-UJI to M-Á.P-G and A.M.P). Author E. Bueichekú was funded by a postdoctoral grant from the "Generalitat Valenciana (2018 APOSTD)" and the "European Social Fund (Investing in your future)".

Background Studies assessing volumetric sex differences have provided contradictory results. Total intracranial volume (TIV) is a major confounding factor when estimating local volumes of interest (VOIs). We investigated how the number, size, and direction of sex differences in gray matter volume (GMv) vary depending on how TIV variation is statistically handled. Methods Sex differences in the GMv of 116 VOIs were assessed in 356 participants (171 females) without correcting for TIV variation or after adjusting the data with 5 different methods (VBM8 non-linear-only modulation, proportions, power-corrected-proportions, covariation, and the residuals method). The outcomes obtained with these procedures were compared to each other and to those obtained in three criterial subsamples, one comparing female-male pairs matched on their TIV and two others comparing groups of either females or males with large/small TIVs. Linear regression was used to quantify TIV effects on raw GMv and the efficacy of each method in controlling for them. Results Males had larger raw GMv than females in all brain areas, but these differences were driven by direct TIV-VOIs relationships and more closely resembled the differences observed between individuals with large/small TIVs of sex-specific subsamples than the sex differences observed in the TIV-matched subsample. All TIV-adjustment methods reduced the number of sex differences but their results were very different. The VBM8- and the proportions-adjustment methods inverted TIV-VOIs relationships and resulted in larger adjusted volumes in females, promoting sex differences largely attributable to TIV variation and very distinct from those observed in the TIV-matched subsample. The other three methods provided results unrelated to TIV and very similar to those of the TIV-matched subsample. In these datasets, sex differences were bidirectional and achieved satisfactory replication rates in 19 VOIs, but they were “small” (d