Relationship between genotype and arcuate fasciculus morphology in six young children with global developmental delay: Preliminary DTI stuy

Abstract

Purpose To investigate whether different genetic mutations observed in children with global developmental delay (GD) are associated with unique patterns of the arcuate fasciculus dysmorphology. Materials and Methods Six children with GD (age: 36.8±14.1 months, 5 boys) having mutations in MID1, CDK4, SFRP1, EN2, RXRG-GLRB, or MECP2, and five children with typical development (TD, age: 38.5±20.5 months, 4 boys) underwent a 3T MRI including diffusion weighted imaging (DWI). Five language pathway segments in the left hemisphere, “C1: Broca's to Wernicke's area", "C2: Broca’s to premotor area", "C3: premotor to Wernicke's area", "C4: Wernicke's to inferior parietal area", and "C5: premotor to inferior parietal area" were objectively identified using the DWI “maximum a posteriori probability” (MAP) classifier. Results Affinity propagation clustering analysis found that three arcuate pathway segments, C1,2,4, of MID1, CDK4, EN2 and MECP2 had a similar pattern of volume ratio while those of SFRP1 and RXRG-GLRB had a heterogeneous pattern of volume ratio (net similarity = −0.01). Using receiver operating characteristic curve analysis, the fiber ratios of C1,2,4 showed a high probability to discriminate between GD and TD, yielding an accuracy of 0.91, 0.91, 1.00, respectively. The fiber volumes of C1 and C4 showed a strong correlation with expressive language (R2=0.6019, p-value=0.033) and receptive language (R2=0.6379, p-value=0.028), respectively. Conclusion The findings of the present study provide preliminary evidence to suggest that different segments of the arcuate fasciculus are formed under the regulation of different genes which, when mutated, may result in developmental delay.

A catalog of single nucleotide changes distinguishing modern humans from archaic hominins

“…We know that archaic hominins likely had certain language-like abilities 123,124 , and hybrids of modern and archaic humans must have survived in their communities 125 , underlining the large overall similarity of these populations. However, genes associated with axon guidance functions, which are important for the refinement of neural circuits including those relevant for speech and language, are found in introgression deserts 126,127 . We suggest that modifications of a complex network in cognition or learning took place in modern human evolution 128 , possibly related to other brain-related 9,16,129,130 , vocal tract 131 or neural changes 132 .…”

A catalog of single nucleotide changes distinguishing modern humans from archaic hominins

“…However, genes associated with axon guidance functions, which are important for the refinement of neural circuits including those relevant for speech and language, are found in introgression deserts (Jeong et al 2016;Lei et al 2017), which seems to be a unidirectional and human-specific pattern especially in the FOXP2 region (Kuhlwilm 2018). We suggest that modifications of a complex network in cognition or learning took place in modern human evolution (Boeckx and Benítez-Burraco 2014), possibly related to other brain-related (Bastir et al 2011;Hublin et al 2015;Boeckx 2017;Bryant and Preuss 2018), vocal tract (Gokhman et al 2017) or neural changes (Belyk and Brown 2017).…”

Deep reasoning neural network analysis to predict language deficits from psychometry‐driven DWI connectome of young children with persistent language concerns

“…For the last decade, there has been a series of neuroimaging studies (Badcock, Bishop, Hardiman, Barry, & Watkins, 2012; Chai, Mattar, Blank, Fedorenko, & Bassett, 2016; Verly et al, 2019) that have utilized MRI protocols to provide objective insights into persistent LI by elucidating neural changes that impair development and/or execution of age‐appropriate levels of language abilities. Our recent MRI studies (Jeong, Sundaram, Behen, & Chugani, 2016a; Jeong, Sundaram, Behen, & Chugani, 2016b; Sundaram, Sivaswamy, Makki, Behen, & Chugani, 2008) have revealed brain network abnormalities in children with LI and further suggested associations between such abnormalities and type, and potentially magnitude, of LI as well. For instance, distinct cortico‐subcortical network abnormalities, identified using whole‐brain connectome analysis (Jeong, Sundaram, et al, 2016a), and involving a frontotemporal language network, differentiated children with LI from healthy controls (Lee, O'Hara, Behen, & Jeong, 2020), and also were differentially associated with distinct LI phenotypes (Lee et al, 2020).…”