Characterizing cardiac autonomic dynamics of fear learning in humans

Understanding transient dynamics of the autonomic nervous system during fear learning remains a critical step to translate basic research into treatment of fear-related disorders. In humans, it has been demonstrated that fear learning typically elicits transient heart rate deceleration. However, classical analyses of heart rate variability (HRV) fail to disentangle the contribution of parasympathetic and sympathetic systems, and crucially, they are not able to capture phasic changes during fear learning. Here, to gain deeper insight into the physiological underpinnings of fear learning, a novel frequency-domain analysis of heart rate was performed using a short-time Fourier transform, and instantaneous spectral estimates extracted from a point-process modeling algorithm. We tested whether spectral transient components of HRV, used as a noninvasive probe of sympathetic and parasympathetic mechanisms, can dissociate between fear conditioned and neutral stimuli. We found that learned fear elicited a transient heart rate deceleration in anticipation of noxious stimuli. Crucially, results revealed a significant increase in spectral power in the high frequency band when facing the conditioned stimulus, indicating increased parasympathetic (vagal) activity, which distinguished conditioned and neutral stimuli during fear learning. Our findings provide a proximal measure of the involvement of cardiac vagal dynamics into the psychophysiology of fear learning and extinction, thus offering new insights for the characterization of fear in mental health and illness

The "Peeking" Effect in Supervised Feature Selection on Diffusion Tensor Imaging Data

We read with great interest the article by Haller et al[1][1] in the February 2013 issue of the American Journal of Neuroradiology . The authors used whole-brain diffusion tensor imaging–derived fractional anisotropy (FA) data, skeletonized through use of the standard tract-based spatia

Early prediction of Autism Spectrum Disorders through interaction analysis in home videos and explainable artificial intelligence

There is considerable discussion about the advantages and disadvantages of early ASD diagnosis. However, the development of easily understandable and administrable tools for teachers or caregivers in order to identify potentially alarming behaviours (red flags) is usually considered valuable even by scholars who are concerned with very early diagnosis. This study proposes an AI pre-screening tool with the aim of creating an easily administrable tool for non-competent observers useful to identify potentially alarming signs in pre-verbal interactions. The use of these features is evaluated using an explainable artificial intelligence algorithm to assess which of the proposed new interaction characteristics were more effective in classifying individuals with ASD vs. controls. We used a rating scale with three core sections - sensorimotor, behavioural, and emotional - each further divided into four items. By seeing home videos of children doing everyday activities, two experienced observers rated each of these items from 1 (highly typical interaction) to 8 (extremely atypical interaction). Then, a machine learning model based on XGBoost was developed for identifying ASD children. The classification obtained was interpreted through the use of SHAP explanations, obtaining an area under the receiver operating curve of 0.938 and 0.914 for the two observers, respectively. These results demonstrated the significance of early detection of body-related sensorimotor features

Fractal Analysis of MRI Data at 7 T: How Much Complex Is the Cerebral Cortex?

The human brain is a highly complex structure, which can be only partially described by conventional metrics derived from magnetic resonance imaging (MRI), such as volume, cortical thickness, and gyrification index. In the last years, the fractal dimension (FD) - a useful quantitative index of fractal geometry - has proven to well express the morphological complexity of the cerebral cortex. However, this complexity is likely higher than that we can observe using MRI scanners with 1.5 T or 3 T field strength. Ultrahigh-field MRI (UHF-MRI) improves imaging of smaller anatomical brain structures by exploring down to a submillimetric spatial resolution with higher signal-to-noise and contrast-to-noise ratios. Accordingly, we hypothesized that UHF-MRI might reveal a higher level of the structural complexity of the cerebral cortex. In this study, using an improved box-counting algorithm, we estimated the FD of the cerebral cortex in six public or private T1-weighted MRI datasets of young healthy subjects (for a total of 87 subjects), acquired at different field strengths (1.5 T, 3 T, and 7 T). Our results showed, for the first time, that MRI-derived FD values of the cerebral cortex imaged at 7 T were significantly higher than those observed at lower field strengths. UHF-MRI provides an anatomical definition not achievable at lower field strengths and can improve unveiling the real structural complexity of the human brain

Artificial intelligence-based models for reconstructing the critical current and index-value surfaces of HTS tapes

For modelling superconductors, interpolation and analytical formulas are commonly used to consider the relationship between the critical current density and other electromagnetic and physical quantities. However, look-up tables are not available in all modelling and coding environments, and interpolation methods must be manually implemented. Moreover, analytical formulas only approximate real physics of superconductors and, in many cases, lack a high level of accuracy. In this paper, we propose a new approach for addressing this problem involving artificial intelligence (AI) techniques for reconstructing the critical surface of high temperature superconducting (HTS) tapes and predicting their index value known as n-value. Different AI models were proposed and implemented, relying on a public experimental database for electromagnetic specifications of HTS tapes, including artificial neural networks (ANN), eXtreme Gradient Boosting (XGBoost), and kernel ridge regressor (KRR). The ANN model was the most accurate in predicting the critical current of HTS materials, performing goodness of fit very close to 1 and extremely low root mean squared error. The XGBoost model proved to be the fastest method, with training computational times under 1 s; whilst KRR could be used as an alternative solution with intermediate performance

Longitudinal study of the effect of a 5-year exercise intervention on structural brain complexity in older adults. A Generation 100 substudy

Physical inactivity has been identified as an important risk factor for dementia. High levels of cardiorespiratory fitness (CRF) have been shown to reduce the risk of dementia. However, the mechanism by which exercise affects brain health is still debated. Fractal dimension (FD) is an index that quantifies the structural complexity of the brain. The purpose of this study was to investigate the effects of a 5-year exercise intervention on the structural complexity of the brain, measured through the FD, in a subset of 105 healthy older adults participating in the randomized controlled trial Generation 100 Study. The subjects were randomized into control, moderate intensity continuous training, and high intensity interval training groups. Both brain MRI and CRF were acquired at baseline and at 1-, 3- and 5-years follow-ups. Cortical thickness and volume data were extracted with FreeSurfer, and FD of the cortical lobes, cerebral and cerebellar gray and white matter were computed. CRF was measured as peak oxygen uptake (VO2peak) using ergospirometry during graded maximal exercise testing. Linear mixed models were used to investigate exercise group differences and possible CRF effects on the brain's structural complexity. Associations between change over time in CRF and FD were performed if there was a significant association between CRF and FD. There were no effects of group membership on the structural complexity. However, we found a positive association between CRF and the cerebral gray matter FD (p < 0.001) and the temporal lobe gray matter FD (p < 0.001). This effect was not present for cortical thickness, suggesting that FD is a more sensitive index of structural changes. The change over time in CRF was associated with the change in temporal lobe gray matter FD from baseline to 5-year follow-up (p < 0.05). No association of the change was found between CRF and cerebral gray matter FD. These results demonstrated that entering old age with high and preserved CRF levels protected against loss of structural complexity in areas sensitive to aging and age-related pathology

Resting state alpha oscillatory activity is a valid and reliable marker of schizotypy

Schizophrenia is among the most debilitating neuropsychiatric disorders. However, clear neurophysiological markers that would identify at-risk individuals represent still an unknown. The aim of this study was to investigate possible alterations in the resting alpha oscillatory activity in normal population high on schizotypy trait, a physiological condition known to be severely altered in patients with schizophrenia. Direct comparison of resting-state EEG oscillatory activity between Low and High Schizotypy Group (LSG and HSG) has revealed a clear right hemisphere alteration in alpha activity of the HSG. Specifically, HSG shows a significant slowing down of right hemisphere posterior alpha frequency and an altered distribution of its amplitude, with a tendency towards a reduction in the right hemisphere in comparison to LSG. Furthermore, altered and reduced connectivity in the right fronto-parietal network within the alpha range was found in the HSG. Crucially, a trained pattern classifier based on these indices of alpha activity was able to successfully differentiate HSG from LSG on tested participants further confirming the specific importance of right hemispheric alpha activity and intrahemispheric functional connectivity. By combining alpha activity and connectivity measures with a machine learning predictive model optimized in a nested stratified cross-validation loop, current research offers a promising clinical tool able to identify individuals at-risk of developing psychosis (i.e., high schizotypy individuals)

Mapping Cortical Degeneration in ALS with Magnetization Transfer Ratio and Voxel-Based Morphometry

Pathological and imaging data indicate that amyotrophic lateral sclerosis (ALS) is a multisystem disease involving several cerebral cortical areas. Advanced quantitative magnetic resonance imaging (MRI) techniques enable to explore in vivo the volume and microstructure of the cerebral cortex in ALS. We studied with a combined voxel-based morphometry (VBM) and magnetization transfer (MT) imaging approach the capability of MRI to identify the cortical areas affected by neurodegeneration in ALS patients. Eighteen ALS patients and 18 age-matched healthy controls were examined on a 1.5T scanner using a high-resolution 3D T1 weighted spoiled gradient recalled sequence with and without MT saturation pulse. A voxel-based analysis (VBA) was adopted in order to automatically compute the regional atrophy and MT ratio (MTr) changes of the entire cerebral cortex. By using a multimodal image analysis MTr was adjusted for local gray matter (GM) atrophy to investigate if MTr changes can be independent of atrophy of the cerebral cortex. VBA revealed several clusters of combined GM atrophy and MTr decrease in motor-related areas and extra-motor frontotemporal cortex. The multimodal image analysis identified areas of isolated MTr decrease in premotor and extra-motor frontotemporal areas. VBM and MTr are capable to detect the distribution of neurodegenerative alterations in the cortical GM of ALS patients, supporting the hypothesis of a multi-systemic involvement in ALS. MT imaging changes exist beyond volume loss in frontotemporal cortices

Prevalence of comorbidities according to predominant phenotype and severity of chronic obstructive pulmonary disease

BACKGROUND: In addition to lung involvement, several other diseases and syndromes coexist in patients with chronic obstructive pulmonary disease (COPD). Our purpose was to investigate the prevalence of idiopathic arterial hypertension (IAH), ischemic heart disease, heart failure, peripheral vascular disease (PVD), diabetes, osteoporosis, and anxious depressive syndrome in a clinical setting of COPD outpatients whose phenotypes (predominant airway disease and predominant emphysema) and severity (mild and severe diseases) were determined by clinical and functional parameters. METHODS: A total of 412 outpatients with COPD were assigned either a predominant airway disease or a predominant emphysema phenotype of mild or severe degree according to predictive models based on pulmonary functions (forced expiratory volume in 1 second/vital capacity; total lung capacity %; functional residual capacity %; and diffusing capacity of lung for carbon monoxide %) and sputum characteristics. Comorbidities were assessed by objective medical records. RESULTS: Eighty-four percent of patients suffered from at least one comorbidity and 75% from at least one cardiovascular comorbidity, with IAH and PVD being the most prevalent ones (62% and 28%, respectively). IAH prevailed significantly in predominant airway disease, osteoporosis prevailed significantly in predominant emphysema, and ischemic heart disease and PVD prevailed in mild COPD. All cardiovascular comorbidities prevailed significantly in predominant airway phenotype of COPD and mild COPD severity. CONCLUSION: Specific comorbidities prevail in different phenotypes of COPD; this fact may be relevant to identify patients at risk for specific, phenotype-related comorbidities. The highest prevalence of comorbidities in patients with mild disease indicates that these patients should be investigated for coexisting diseases or syndromes even in the less severe, pauci-symptomatic stages of COPD. The simple method employed to phenotype and score COPD allows these results to be translated easily into daily clinical practice

Regional Analysis of the Magnetization Transfer Ratio of the Brain in Mild Alzheimer Disease and Amnestic Mild Cognitive Impairment

BACKGROUND AND PURPOSE: Manually drawn VOI-based analysis shows a decrease in magnetization transfer ratio in the hippocampus of patients with Alzheimer disease. We investigated with whole-brain voxelwise analysis the regional changes of the magnetization transfer ratio in patients with mild Alzheimer disease and patients with amnestic mild cognitive impairment. MATERIALS AND METHODS: Twenty patients with mild Alzheimer disease, 27 patients with amnestic mild cognitive impairment, and 30 healthy elderly control subjects were examined with high-resolution T1WI and 3-mm-thick magnetization transfer images. Whole-brain voxelwise analysis of magnetization transfer ratio maps was performed by use of Statistical Parametric Mapping 8 software and was supplemented by the analysis of the magnetization transfer ratio in FreeSurfer parcellation-derived VOIs. RESULTS: Voxelwise analysis showed 2 clusters of significantly decreased magnetization transfer ratio in the left hippocampus and amygdala and in the left posterior mesial temporal cortex (fusiform gyrus) of patients with Alzheimer disease as compared with control subjects but no difference between patients with amnestic mild cognitive impairment and either patients with Alzheimer disease or control subjects. VOI analysis showed that the magnetization transfer ratio in the hippocampus and amygdala was significantly lower (bilaterally) in patients with Alzheimer disease when compared with control subjects (ANOVA with Bonferroni correction, at P < .05). Mean magnetization transfer ratio values in the hippocampus and amygdala in patients with amnestic mild cognitive impairment were between those of healthy control subjects and those of patients with mild Alzheimer disease. Support vector machine-based classification demonstrated improved classification performance after inclusion of magnetization transfer ratio-related features, especially between patients with Alzheimer disease versus healthy subjects. CONCLUSIONS: Bilateral but asymmetric decrease of magnetization transfer ratio reflecting microstructural changes of the residual GM is present not only in the hippocampus but also in the amygdala in patients with mild Alzheimer disease