New insights into the human brain’s cognitive organization : Views from the top, from the bottom, from the left and, particularly, from the right

The view that the left cerebral hemisphere in humans “dominates” over the “subdominant” right hemisphere has been so deeply entrenched in neuropsychology that no amount of evidence seems able to overcome it. In this article, we examine inhibitory cause-and-effect connectivity among human brain structures related to different parts of the triune evolutionary stratification —archicortex, paleocortex and neocortex— in relation to early and late phases of a prolonged resting-state functional magnetic resonance imaging (fMRI) experiment. With respect to the evolutionarily youngest parts of the human cortex, the left and right frontopolar regions, we also provide data on the asymmetries in underlying molecular mechanisms, namely on the differential expression of the protein-coding genes and regulatory microRNA sequences. In both domains of research, our results contradict the established view by demonstrating a pronounced right-to-left vector of causation in the hemispheric interaction at multiple levels of brain organization. There may be several not mutually exclusive explanations for the evolutionary significance of this pattern of lateralization. One of the explanations emphasizes the computational advantage of separating the neural substrates for processing novel information ("exploration") mediated predominantly by the right hemisphere, and processing with reliance on established cognitive routines and representations ("exploitation") mediated predominantly by the left hemisphere.publishedVersio

New insights into the human brain’s cognitive organization : Views from the top, from the bottom, from the left and, particularly, from the right

The view that the left cerebral hemisphere in humans “dominates” over the “subdominant” right hemisphere has been so deeply entrenched in neuropsychology that no amount of evidence seems able to overcome it. In this article, we examine inhibitory cause-and-effect connectivity among human brain structures related to different parts of the triune evolutionary stratification —archicortex, paleocortex and neocortex— in relation to early and late phases of a prolonged resting-state functional magnetic resonance imaging (fMRI) experiment. With respect to the evolutionarily youngest parts of the human cortex, the left and right frontopolar regions, we also provide data on the asymmetries in underlying molecular mechanisms, namely on the differential expression of the protein-coding genes and regulatory microRNA sequences. In both domains of research, our results contradict the established view by demonstrating a pronounced right-to-left vector of causation in the hemispheric interaction at multiple levels of brain organization. There may be several not mutually exclusive explanations for the evolutionary significance of this pattern of lateralization. One of the explanations emphasizes the computational advantage of separating the neural substrates for processing novel information ("exploration") mediated predominantly by the right hemisphere, and processing with reliance on established cognitive routines and representations ("exploitation") mediated predominantly by the left hemisphere

Integrative bioinformatics and artificial intelligence analyses of transcriptomics data identified genes associated with major depressive disorders including NRG1

Major depressive disorder (MDD) is a common mental disorder and is amongst the most prevalent psychiatric disorders. MDD remains challenging to diagnose and predict its onset due to its heterogeneous phenotype and complex etiology. Hence, early detection using diagnostic biomarkers is critical for rapid intervention. In this study, a mixture of AI and bioinformatics were used to mine transcriptomic data from publicly available datasets including 170 MDD patients and 121 healthy controls. Bioinformatics analysis using gene set enrichment analysis (GSEA) and machine learning (ML) algorithms were applied. The GSEA revealed that differentially expressed genes in MDD patients are mainly enriched in pathways related to immune response, inflammatory response, neurodegeneration pathways and cerebellar atrophy pathways. Feature selection methods and ML provided predicted models based on MDD-altered genes with ≥75% of accuracy. The integrative analysis between the bioinformatics and ML approaches identified ten key MDD-related biomarkers including NRG1, CEACAM8, CLEC12B, DEFA4, HP, LCN2, OLFM4, SERPING1, TCN1 and THBS1. Among them, NRG1, active in synaptic plasticity and neurotransmission, was the most robust and reliable to distinguish between MDD patients and healthy controls amongst independent external datasets consisting of a mixture of populations. Further evaluation using saliva samples from an independent cohort of MDD and healthy individuals confirmed the upregulation of NRG1 in patients with MDD compared to healthy controls. Functional mapping to the human brain regions showed NRG1 to have high expression in the main subcortical limbic brain regions implicated in depression. In conclusion, integrative bioinformatics and ML approaches identified putative non-invasive diagnostic MDD-related biomarkers panel for the onset of depression