Scaling of brain compartments to brain size

In this study, we examine the relationship between total brain volume (BV) and the volumes of several main brain compartmental (BC) measures (cortical thickness, cortical surface area, corpus callosum, cortical gray matter, normal appearing cerebral white matter (NAWM), amygdala, accumbens, caudate, hippocampus, putamen, pallidum, thalamus, cerebellar gray matter, and cerebellar WM) of physically and cognitively healthy elderly individuals (mean age: 71 years, age range: 65-85 years). The statistical analysis uncovered extremely different relationships between total BV and the aforementioned BC metrics. These relationships ranged from extremely strong (BV explaining 85% of the variability of cerebral WM volume) to a very small relationship (for the caudate volume and the cortical thickness). In addition, cerebral WM and the accumbens volumes scaled out of proportion with BV, whereas most other BC measures scaled less than proportional to BV. Thus, larger brains exhibit relatively larger cerebral NAWM and accumbens volumes than do smaller brains. Cortical gray matter (and most other BC measures), on the other hand, relatively decreases as BV increases, resulting in relatively small cortical gray matter volumes (and relatively small BC measures) for large brains. These relationships are discussed within the context of general allometric scaling principles for the human brain. In addition, possible methodological consequences of analyzing anatomical data on the basis of MRI measurements are also discussed

Decline variability of cortical and subcortical regions in aging: a longitudinal study

Describing the trajectories of age-related change for different brain structures has been of interest in many recent studies. However, our knowledge regarding these trajectories and their associations is still limited due to small sample sizes and low numbers of repeated measures. For the present study, we used a large longitudinal dataset (four measurements over 4 years) comprising anatomical data from a sample of healthy older adults (N = 231 at baseline). This dataset enables us to gain new insights about volumetric cortical and subcortical changes and their associations in the context of healthy aging. Brain structure volumes were derived from T1-weighted MRI scans using FreeSurfer segmentation tools. Brain structure trajectories were fitted using mixed models and latent growth curve models to gain information about the mean extent and variability of decline trajectories for different brain structures as well as the associations between individual trajectories. On the group level, our analyses indicate similar linear changes for frontal and parietal brain regions, while medial temporal regions showed an accelerated decline with advancing age. Regarding subcortical regions, some structures showed strong declines (e.g., hippocampus), others showed little decline (e.g., pallidum). Our data provide little evidence for sex differences regarding the aforementioned trajectories. Between-person variability of the person-specific slopes (random slopes) was largest in subcortical and medial temporal brain structures. When looking at the associations between the random slopes from each brain structure, we found that the decline is largely homogenous across the majority of cortical brain structures. In subcortical and medial temporal brain structures, however, more heterogeneity of the decline was observed, meaning that the extent of the decline in one structure is less predictive of the decline in another structure. Taken together, our study contributes to enhancing our understanding of structural brain aging by demonstrating (1) that average volumetric change differs across the brain and (2) that there are regional differences with respect to between-person variability in the slopes. Moreover, our data suggest (3) that random slopes are highly correlated across large parts of the cerebral cortex but (4) that some brain regions (i.e., medial temporal regions) deviate from this homogeneity

Object-Location Memory Training in Older Adults Leads to Greater Deactivation of the Dorsal Default Mode Network

Substantial evidence indicates that cognitive training can be efficacious for older adults, but findings regarding training-related brain plasticity have been mixed and vary depending on the imaging modality. Recent years have seen a growth in recognition of the importance of large-scale brain networks on cognition. In particular, task-induced deactivation within the default mode network (DMN) is thought to facilitate externally directed cognition, while aging-related decrements in this neural process are related to reduced cognitive performance. It is not yet clear whether task-induced deactivation within the DMN can be enhanced by cognitive training in the elderly. We previously reported durable cognitive improvements in a sample of healthy older adults (age range = 60-75) who completed 6 weeks of process-based object-location memory training (N = 36) compared to an active control training group (N = 31). The primary aim of the current study is to evaluate whether these cognitive gains are accompanied by training-related changes in task-related DMN deactivation. Given the evidence for heterogeneity of the DMN, we examine task-related activation/deactivation within two separate DMN branches, a ventral branch related to episodic memory and a dorsal branch more closely resembling the canonical DMN. Participants underwent functional magnetic resonance imaging (fMRI) while performing an untrained object-location memory task at four time points before, during, and after the training period. Task-induced (de)activation values were extracted for the ventral and dorsal DMN branches at each time point. Relative to visual fixation baseline: (i) the dorsal DMN was deactivated during the scanner task, while the ventral DMN was activated; (ii) the object-location memory training group exhibited an increase in dorsal DMN deactivation relative to the active control group over the course of training and follow-up; (iii) changes in dorsal DMN deactivation did not correlate with task improvement. These results indicate a training-related enhancement of task-induced deactivation of the dorsal DMN, although the specificity of this improvement to the cognitive task performed in the scanner is not clear

Reducing the Interval Between Volume Acquisitions Improves "Sparse” Scanning Protocols in Event-related Auditory fMRI

Sparse and clustered-sparse temporal sampling fMRI protocols have been devised to reduce the influence of auditory scanner noise in the context of auditory fMRI studies. Here, we report an improvement of the previously established clustered-sparse acquisition scheme. The standard procedure currently used by many researchers in the field is a scanning protocol that includes relatively long silent pauses between image acquisitions (and therefore, a relatively long repetition time or cluster-onset asynchrony); it is during these pauses that stimuli are presented. This approach makes it unlikely that stimulus-induced BOLD response is obscured by scanner-noise-induced BOLD response. It also allows the BOLD response to drop near baseline; thus, avoiding saturation of BOLD signal and theoretically increasing effect size. A possible drawback of this approach is the limited number of stimulus presentations and image acquisitions that are possible in a given period of time, which could result in an inaccurate estimation of effect size (higher standard error). Since this line of reasoning has not yet been empirically tested, we decided to vary the cluster-onset asynchrony (7.5, 10, 12.5, and 15s) in the context of a clustered-sparse protocol. In this study sixteen healthy participants listened to spoken sentences. We performed whole-brain fMRI group statistics and region of interest analysis with anatomically defined regions of interest (auditory core and association areas). We discovered that the protocol, which included a short cluster-onset asynchrony (7.5s), yielded more advantageous results than the other protocols, which involved longer cluster-onset asynchrony. The short cluster-onset asynchrony protocol exhibited a larger number of activated voxels and larger mean effect sizes with lower standard errors. Our findings suggest that, contrary to prior experience, a short cluster-onset asynchrony is advantageous because more stimuli can be delivered within any given period of time. Alternatively, a given number of stimuli can be presented in less time, and this broadens the spectrum of possible fMRI application

Cortical Surface Area and Cortical Thickness Demonstrate Differential Structural Asymmetry in Auditory-Related Areas of the Human Cortex

This investigation provides an analysis of structural asymmetries in 5 anatomically defined regions (Heschl's gyrus, HG; Heschl's sulcus, HS; planum temporale, PT; planum polare, PP; superior temporal gyrus, STG) within the human auditory-related cortex. Volumetric 3-dimensional T1-weighted magnetic resonance imaging scans were collected from 104 participants (52 males). Cortical volume (CV), cortical thickness (CT), and cortical surface area (CSA) were calculated based on individual scans of these anatomical traits. This investigation demonstrates a leftward asymmetry for CV and CSA that is observed in the HG, STG, and PT regions. As regards CT, we note a rightward asymmetry in the HG and HS. A correlation analysis of asymmetry indices between measurements for distinct regions of interest (ROIs) yields significant correlations between CT and CV in 4 of 5 ROIs (HG, HS, PT, and STG). Significant correlation values between CSA and CV are observed for all 5 ROIs. The findings suggest that auditory-related cortical areas demonstrate larger leftward asymmetry with respect to the CSA, while a clear rightward asymmetry with respect to CT is salient in both the primary and the secondary auditory cortex only. In addition, we propose that CV is not an ideal neuromarker for anatomical measurements. CT and CSA should be considered independent traits of anatomical asymmetries in the auditory-related corte

Age-related differences in auditory evoked potentials as a function of task modulation during speech-nonspeech processing

Background Healthy aging is typically associated with impairment in various cognitive abilities such as memory, selective attention or executive functions. Less well observed is the fact that also language functions in general and speech processing in particular seems to be affected by age. This impairment is partly caused by pathologies of the peripheral auditory nervous system and central auditory decline and in some part also by a cognitive decay. Aims This cross-sectional electroencephalography (EEG) study investigates temporally early electrophysiological correlates of auditory related selective attention in young (20–32 years) and older (60–74 years) healthy adults. Material and methods In two independent tasks, we systematically modulate the subjects' focus of attention by presenting words and pseudowords as targets and white noise stimuli as distractors. Results Behavioral data showed no difference in task accuracy between the two age samples irrespective of the modulation of attention. However, our work is the first to show that the N1- and the P2 component evoked by speech and nonspeech stimuli are specifically modulated in older adults and young adults depending on the subjects' focus of attention. Conclusion This finding is particularly interesting in that the age-related differences in AEPs may be reflecting levels of processing that are not mirrored by the behavioral measurements

Associations of subclinical cerebral small vessel disease and processing speed in non-demented subjects: A 7-year study

Markers of cerebral small vessel disease (CSVD) have previously been associated with age-related cognitive decline. Using longitudinal data of cognitively healthy, older adults (N = 216, mean age at baseline = 70.9 years), we investigated baseline status and change in white matter hyperintensities (WMH) (total, periventricular, deep), normal appearing white matter (NAWM), brain parenchyma volume (BPV) and processing speed over seven years as well as the impact of different covariates by applying latent growth curve (LGC) models. Generally, we revealed a complex pattern of associations between the different CSVD markers. More specifically, we observed that changes of deep WMH (dWMH), as compared to periventricular WMH (pWMH), were more strongly related to the changes of other CSVD markers and also to baseline processing speed performance. Further, the number of lacunes rather than their volume reflected the severity of CSVD. With respect to the studied covariates, we revealed that higher education had a protective effect on subsequent total WMH, pWMH, lacunar number, NAWM volume, and processing speed performance. The indication of antihypertensive drugs was associated with lower lacunar number and volume at baseline and the indication of antihypercholesterolemic drugs came along with higher processing speed performance at baseline. In summary, our results confirm previous findings, and extend them by providing information on true within-person changes, relationships between the different CSVD markers and brain-behavior associations. The moderate to strong associations between changes of the different CSVD markers indicate a common pathological relationship and, thus, support multidimensional treatment strategies

Predicting future cognitive decline from non-brain and multimodal brain imaging data in healthy and pathological aging

Previous literature has focused on predicting a diagnostic label from structural brain imaging. Since subtle changes in the brain precede a cognitive decline in healthy and pathological aging, our study predicts future decline as a continuous trajectory instead. Here, we tested whether baseline multimodal neuroimaging data improve the prediction of future cognitive decline in healthy and pathological aging. Nonbrain data (demographics, clinical, and neuropsychological scores), structural MRI, and functional connectivity data from OASIS-3 (N = 662; age = 46–96 years) were entered into cross-validated multitarget random forest models to predict future cognitive decline (measured by CDR and MMSE), on average 5.8 years into the future. The analysis was preregistered, and all analysis code is publicly available. Combining non-brain with structural data improved the continuous prediction of future cognitive decline (best test-set performance: R2 = 0.42). Cognitive performance, daily functioning, and subcortical volume drove the performance of our model. Including functional connectivity did not improve predictive accuracy. In the future, the prognosis of age-related cognitive decline may enable earlier and more effective individualized cognitive, pharmacological, and behavioral interventions

Hemispheric asymmetries in resting-state EEG and fMRI are related to approach and avoidance behaviour, but not to eating behaviour or BMI

Much of our behaviour is driven by two motivational dimensions-approach and avoidance. These have been related to frontal hemispheric asymmetries in clinical and resting-state EEG studies: Approach was linked to higher activity of the left relative to the right hemisphere, while avoidance was related to the opposite pattern. Increased approach behaviour, specifically towards unhealthy foods, is also observed in obesity and has been linked to asymmetry in the framework of the right-brain hypothesis of obesity. Here, we aimed to replicate previous EEG findings of hemispheric asymmetries for self-reported approach/avoidance behaviour and to relate them to eating behaviour. Further, we assessed whether resting fMRI hemispheric asymmetries can be detected and whether they are related to approach/avoidance, eating behaviour and BMI. We analysed three samples: Sample 1 (n = 117) containing EEG and fMRI data from lean participants, and Samples 2 (n = 89) and 3 (n = 152) containing fMRI data from lean, overweight and obese participants. In Sample 1, approach behaviour in women was related to EEG, but not to fMRI hemispheric asymmetries. In Sample 2, approach/avoidance behaviours were related to fMRI hemispheric asymmetries. Finally, hemispheric asymmetries were not related to either BMI or eating behaviour in any of the samples. Our study partly replicates previous EEG findings regarding hemispheric asymmetries and indicates that this relationship could also be captured using fMRI. Our findings suggest that eating behaviour and obesity are likely to be mediated by mechanisms not directly relating to frontal asymmetries in neuronal activation quantified with EEG and fMRI.Peer reviewe

Automated individual-level parcellation of Broca's region based on functional connectivity

Broca's region can be subdivided into its constituent areas 44 and 45 based on established differences in connectivity to superior temporal and inferior parietal regions. The current study builds on our previous work manually parcellating Broca's area on the individual-level by applying these anatomical criteria to functional connectivity data. Here we present an automated observer-independent and anatomy-informed parcellation pipeline with comparable precision to the manual labels at the individual-level. The method first extracts individualized connectivity templates of areas 44 and 45 by assigning to each surface vertex within the ventrolateral frontal cortex the partial correlation value of its functional connectivity to group-level templates of areas 44 and 45, accounting for other template connectivity patterns. To account for cross-subject variability in connectivity, the partial correlation procedure is then repeated using individual-level network templates, including individual-level connectivity from areas 44 and 45. Each node is finally labeled as area 44, 45, or neither, using a winner-take-all approach. The method also incorporates prior knowledge of anatomical location by weighting the results using spatial probability maps. The resulting area labels show a high degree of spatial overlap with the gold-standard manual labels, and group-average area maps are consistent with cytoarchitectonic probability maps of areas 44 and 45. To facilitate reproducibility and to demonstrate that the method can be applied to resting-state fMRI datasets with varying acquisition and preprocessing parameters, the labeling procedure is applied to two open-source datasets from the Human Connectome Project and the Nathan Kline Institute Rockland Sample. While the current study focuses on Broca's region, the method is adaptable to parcellate other cortical regions with distinct connectivity profiles