Search

Bilingualism has been shown to contribute to increased resilience against cognitive aging. One of the key brain structures linked to memory and dementia symptom onset, the hippocampus, has been observed to adapt in response to bilingual experience – at least in healthy individuals. However, in the context of neurodegenerative pathology, it is yet unclear what role previous bilingual experience might have in terms of sustaining integrity of this structure or related behavioral correlates. The present study adds to the limited cohort of research on the effects of bilingualism on neurocognitive outcomes in Mild Cognitive Impairment (MCI) using structural brain data. We investigate whether bilingual language experience (operationalized as language entropy) results in graded neurocognitive adaptations within a cohort of bilinguals diagnosed with MCI. Results reveal a non-linear effect of bilingual language entropy on hippocampal volume, although they do not predict episodic memory performance, nor age of MCI diagnosis.

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.