Near infrared spectroscopy (NIRS) is an increasingly popular technology for studying brain function. NIRS presents several advantages relative to functional magnetic resonance imaging (fMRI), such as measurement of concentration changes in both oxygenated- and deoxygenated hemoglobin, finer temporal resolution, and ease of administration, as well as disadvantages, most prominently inferior spatial resolution and decreased signal-to-noise ratio (SNR). While fMRI has become the gold standard for in vivo imaging of the human brain, in practice NIRS is a more convenient and less expensive technology than fMRI. It is therefore of interest to many researchers how NIRS compares to fMRI in studies of brain function. In the present study we scanned participants with simultaneous NIRS and fMRI on a battery of cognitive tasks, placing NIRS probes over both frontal and parietal brain regions. We performed detailed comparisons of the signals in both temporal and spatial domains. We found that NIRS signals have significantly weaker SNR, but are nonetheless often highly correlated with fMRI measurements. Both SNR and the distance between the scalp and the brain contributed to variability in the NIRS/fMRI correlations. In the spatial domain, we found that a photon path forming an ellipse between the NIRS emitter and detector correlated most strongly with the BOLD response. Taken together these findings suggest that, while NIRS can be an appropriate substitute for fMRI for studying brain activity related to cognitive tasks, care should be taken when designing studies with NIRS to ensure that: 1) the spatial resolution is adequate for answering the question of interest and 2) the design accounts for weaker SNR, especially in brain regions more distal from the scalp.

We used Near-Infrared Spectroscopy (NIRS) to simultaneously measure brain activity in two people while they played a computer-based cooperation game side by side. Inter-brain activity coherence was calculated between the two participants. We found that the coherence between signals generated by participants right superior frontal cortices increased during cooperation, but not during competition. Increased coherence was also associated with better cooperation performance. To our knowledge, this work represents the first use of a single NIRS instrument for simultaneous measurements of brain activity in two people. This study demonstrates the use of NIRS-based hyperscanning in studies of social interaction in a naturalistic environment.

Klinefelter Syndrome (KS) is a genetic disorder characterized by a supernumerary X chromosome. As such, KS offers a naturally occurring human model for the study of both X-chromosome gene expression and androgen on brain development. Previous neuroimaging studies reveal neuroanatomical variations associated with KS, but differ widely with respect to subject inclusion criteria, including mosaicism, pubertal status, and history of testosterone replacement therapy (TRT), all factors likely to influence neurodevelopment. We conducted a voxel-based morphometry (VBM) study of regional grey and white matter volumes in 31 KS males (mean age: 9.69 years ± SD: 1.70) and 36 typically developing (TD) male controls (10.99 ± 1.72). None of the participants with KS had received TRT, and all were prepubertal and had non-mosaic 47, XXY karyotypes. After controlling for age, males with KS showed trends (0.05

Functional near-infrared spectroscopy (fNIRS) is an increasingly popular technology for studying brain function because it is non-invasive, non-irradiating and relatively inexpensive. Further, fNIRS potentially allows measurement of hemodynamic activity with high temporal resolution (milliseconds) and in naturalistic settings. However, in comparison with other imaging modalities, namely fMRI, fNIRS has a significant drawback: limited sensitivity to hemodynamic changes in deep-brain regions. To overcome this limitation, we developed a computational method to infer deep-brain activity using fNIRS measurements of cortical activity. Using simultaneous fNIRS and fMRI, we measured brain activity in 17 participants as they completed three cognitive tasks. A support vector regression (SVR) learning algorithm was used to predict activity in twelve deep-brain regions using information from surface fNIRS measurements. We compared these predictions against actual fMRI-measured activity using Pearson's correlation to quantify prediction performance. To provide a benchmark for comparison, we also used fMRI measurements of cortical activity to infer deep-brain activity. When using fMRI-measured activity from the entire cortex, we were able to predict deep-brain activity in the fusiform cortex with an average correlation coefficient of 0.80 and in all deep-brain regions with an average correlation coefficient of 0.67. The top 15% of predictions using fNIRS signal achieved an accuracy of 0.7. To our knowledge, this study is the first to investigate the feasibility of using cortical activity to infer deep-brain activity. This new method has the potential to extend fNIRS applications in cognitive and clinical neuroscience research.

AIM To assess global and regional brain matter variations associated with XYY syndrome by comparison with Klinefelter syndrome and typical development. METHOD We used two conceptually distinct voxel-based magnetic resonance imaging methods to examine brain structure in young males with XYY syndrome: (1) volumetric comparison to assess global grey and white matter volumes and (2) support vector machine-based multivariate pattern classification analysis to assess regional neuroanatomy. We assessed verbal, non-verbal, and spatial abilities with the Differential Ability Scales (DAS), and we measured autism diagnostic criteria in eight males with XYY syndrome using the Social Responsiveness Scale and the Autism Diagnostic Interview-Revised (ADI-R). RESULTS A comparison of 36 typically developing males (mean age 11y, SD 1y 9mo), 31 males with Klinefelter syndrome (mean age 9y 8mo, SD 1y 8mo), and eight males with XYY syndrome (mean age 11y 6mo, SD 1y 11mo) showed that total white and grey matter volumes were significantly, or nearly significantly, higher in males with XYY syndrome than in males belonging to the other two groups (grey matter: XYY males vs typically developing males, p<0.006; XYY vs males with Klinefelter syndrome, p<0.001; white matter: XYY males vs typically developing males, p=0.061; XYY males vs males with Klinefelter syndrome, p=0.004). Voxel-based multivariate pattern classification analysis indicates that, after controlling for global volumes, regional brain variations in XYY syndrome are more like those found in Klinefelter syndrome than those occurring in typical development. Further, visualization of classification parameters suggests that insular and frontotemporal grey matter and white matter, including known language areas, are reduced in males with XYY syndrome, similar to what is seen in Klinefelter syndrome. In males with XYY syndrome, DAS verbal and non-verbal scores were significantly lower than in typically developing participants (both p<0.001). DAS scores were not significantly different between XYY and Klinefelter syndrome groups. In five of eight males with XYY syndrome, the Social Responsiveness Scale score exceeded the cut-off for a likely diagnosis of autism spectrum disorder (ASD). In three of eight males with XYY syndrome, the ADI-R score met the cut-off for ASD diagnosis; in another two, ADI-R scores within the social and communication domains met the cut-off values for a diagnosis of ASD. INTERPRETATION The results suggest that genetic variations associated with XYY syndrome result in increased brain matter volumes, a finding putatively related to the increased frequency of ASDs in individuals with this condition. In addition, frontotemporal grey and white matter reductions in XYY syndrome provide a likely neuroanatomical correlate for observed language impairments.