Brain signals such as electroencephalogram (EEG) often show oscillations at various frequencies, which are represented as distinct “bumps” in the power spectral density (PSD) of these signals. In addition, the PSD also shows a distinct reduction in power with increasing frequency, which pertains to aperiodic activity and is often termed as the “1/f” component. While a change in periodic activity in brain signals with healthy aging and mental disorders has been reported, recent studies have shown a reduction in the slope of the aperiodic activity with these factors as well. However, these studies only analysed PSD slopes over a limited frequency range (<100 Hz). To test whether the PSD slope is affected over a wider frequency range with aging and mental disorder, we collected EEG data with high sampling rate (2500 Hz) from a large population of elderly subjects (>49 years) who were healthy (N=217) or had mild cognitive impairment (MCI; N=11) or Alzheimer’s Disease (AD; N=5), and analysed the PSD slope till 800 Hz. Consistent with previous studies, the 1/f slope up to ~150 Hz reduced with healthy aging. Surprisingly, we found the opposite at higher frequencies (>200 Hz): the slope increased with age. This result was observed in all electrodes, for both eyes open and eyes closed conditions, and for different reference schemes. Slopes were not significantly different in MCI/AD subjects compared to age and gender matched healthy controls. Overall, our results constrain the biophysical mechanisms that are reflected in the PSD slopes in healthy and pathological aging. Significance Statement Aperiodic activity in the brain is characterized by measuring the slope of the power spectrum of brain signals. This slope has been shown to flatten with healthy aging, suggesting an increase in some sort of “neural noise”. However, this flattening has been observed only over a limited frequency range (<150 Hz). We found that at higher frequencies (>200 Hz), the opposite happens: the slope steepens with age. This occurs at all electrodes, irrespective of state and referencing techniques. However, the slope is unchanged in subjects with early Alzheimer’s Disease (AD) and their controls. Our results shed new light on the properties of neural noise and the neurophysiological processes affecting AD and the aperiodic activity. ### Competing Interest Statement The authors have declared no competing interest.
Neuroinflammation is both a consequence and driver of overfeeding and weight gain in rodent obesity models. Advances in magnetic resonance imaging (MRI) enable investigations of brain microstructure that suggests neuroinflammation in human obesity. To assess the convergent validity across MRI techniques and extend previous findings, we used diffusion basis spectrum imaging (DBSI) to characterize obesity-associated alterations in brain microstructure in 601 children (age 9-11 years) from the Adolescent Brain Cognitive DevelopmentSM Study. Compared with children with normal-weight, greater DBSI restricted fraction (RF), reflecting neuroinflammation-related cellularity, was seen in widespread white matter in children with overweight and obesity. Greater DBSI-RF in hypothalamus, caudate nucleus, putamen, and, in particular, nucleus accumbens, correlated with higher baseline body mass index and related anthropometrics. Comparable findings were seen in the striatum with a previously reported restriction spectrum imaging (RSI) model. Gain in waist circumference over 1 and 2 years related, at nominal significance, to greater baseline RSI-assessed restricted diffusion in nucleus accumbens and caudate nucleus, and DBSI-RF in hypothalamus, respectively. Here we demonstrate that childhood obesity is associated with microstructural alterations in white matter, hypothalamus, and striatum. Our results also support the reproducibility, across MRI methods, of findings of obesity-related putative neuroinflammation in children.
Abstract In Parkinson’s disease (PD), reduced cerebral cortical thickness may reflect network-based degeneration. This study performed cognitive assessment and brain MRI in 30 PD participants and 41 controls at baseline and 18 months later. We hypothesized that cerebral cortical thickness and volume, as well as change in these metrics, would differ between PD participants who remained cognitively stable and those who experienced cognitive decline. Dividing the participant sample into PD-stable, PD-decline, and control-stable groups, we compared mean cortical thickness and volume within segments that comprise the prefrontal cognitive-control, memory, dorsal spatial, and ventral object-based networks at baseline. We then compared the rate of change in cortical thickness and volume between the same groups using a vertex-wise approach. We found that the PD-decline group had lower cortical thickness within all 4 cognitive networks in comparison with controls, as well as lower cortical thickness within the prefrontal and medial temporal networks in comparison with the PD-stable group. The PD-decline group also experienced a greater rate of volume loss in the lateral temporal cortices in comparison with the control group. This study suggests that lower thickness and volume in prefrontal, medial, and lateral temporal regions may portend cognitive decline in PD.
Despite their anatomical and functional distinctions, there is growing evidence that the dorsal and ventral visual pathways interact to support object recognition. However, the exact nature of these interactions remains poorly understood. Is the presence of identity-relevant object information in the dorsal pathway simply a byproduct of ventral input? Or, might the dorsal pathway be a source of input to the ventral pathway for object recognition? In the current study, we used high-density EEG-a technique with high temporal precision and spatial resolution sufficient to distinguish parietal and temporal lobes-to characterise the dynamics of dorsal and ventral pathways during object viewing. Using multivariate analyses, we found that category decoding in the dorsal pathway preceded that in the ventral pathway. Importantly, the dorsal pathway predicted the multivariate responses of the ventral pathway in a time-dependent manner, rather than the other way around. Together, these findings suggest that the dorsal pathway is a critical source of input to the ventral pathway for object recognition.
The ability to process verbal language seems unique to humans and relies not only on semantics but on other forms of communication such as affective vocalisations, that we share with other primate species—particularly great apes ( Hominidae ). To better understand these processes at the behavioural and brain level, we asked human participants to categorize vocalizations of four primate species including human, great apes (chimpanzee and bonobo), and monkey (rhesus macaque) during MRI acquisition. Classification was above chance level for all species but bonobo vocalizations. Imaging analyses were computed using a participant-specific, trial-by-trial fitted probability categorization value in a model-based style of data analysis. Model-based analyses revealed the implication of the bilateral orbitofrontal cortex and inferior frontal gyrus pars triangularis (IFG tri ) respectively correlating and anti-correlating with the fitted probability of accurate species classification. Further conjunction analyses revealed enhanced activity in a sub-area of the left IFG tri specifically for the accurate classification of chimpanzee calls compared to human voices. Our data therefore reveal distinct frontal mechanisms that shed light on how the human brain evolved to process non-verbal language. Author contributions CD and LC were involved in every steps of the study including experimental design, programming, data acquisition, data analysis and redaction of the first draft of the manuscript and subsequent editing. EP helped design the model-based MRI analyses and interpret the results. TG and DG were involved in the experimental design and study planification. All authors wrote and edited the manuscript.
