A multi-site study on sex differences in cortical thickness in non-demented Parkinson's disease

Clinical, cognitive, and atrophy characteristics depending on sex have been previously reported in Parkinson's disease (PD). However, though sex differences in cortical gray matter measures in early drug naïve patients have been described, little is known about differences in cortical thickness (CTh) as the disease advances. Our multi-site sample comprised 211 non-demented PD patients (64.45% males; mean age 65.58 ± 8.44 years old; mean disease duration 6.42 ± 5.11 years) and 86 healthy controls (50% males; mean age 65.49 ± 9.33 years old) with available T1-weighted 3 T MRI data from four international research centers. Sex differences in regional mean CTh estimations were analyzed using generalized linear models. The relation of CTh in regions showing sex differences with age, disease duration, and age of onset was examined through multiple linear regression. PD males showed thinner cortex than PD females in six frontal (bilateral caudal middle frontal, bilateral superior frontal, left precentral and right pars orbitalis), three parietal (bilateral inferior parietal and left supramarginal), and one limbic region (right posterior cingulate). In PD males, lower CTh values in nine out of ten regions were associated with longer disease duration and older age, whereas in PD females, lower CTh was associated with older age but with longer disease duration only in one region. Overall, male patients show a more widespread pattern of reduced CTh compared with female patients. Disease duration seems more relevant to explain reduced CTh in male patients, suggesting worse prognostic over time. Further studies should explore sex-specific cortical atrophy trajectories using large longitudinal multi-site data.

Cortical thickness modeling and variability in Alzheimer's disease and frontotemporal dementia

BACKGROUND AND OBJECTIVE

Alzheimer's disease (AD) and frontotemporal dementia (FTD) show different patterns of cortical thickness (CTh) loss compared with healthy controls (HC), even though there is relevant heterogeneity between individuals suffering from each of these diseases. Thus, we developed CTh models to study individual variability in AD, FTD, and HC.

METHODS

We used the baseline CTh measures of 379 participants obtained from the structural MRI processed with FreeSurfer. A total of 169 AD patients (63 ± 9 years, 65 men), 88 FTD patients (64 ± 9 years, 43 men), and 122 HC (62 ± 10 years, 47 men) were studied. We fitted region-wise temporal models of CTh using Support Vector Regression. Then, we studied associations of individual deviations from the model with cerebrospinal fluid levels of neurofilament light chain (NfL) and 14-3-3 protein and Mini-Mental State Examination (MMSE). Furthermore, we used real longitudinal data from 144 participants to test model predictivity.

RESULTS

We defined CTh spatiotemporal models for each group with a reliable fit. Individual deviation correlated with MMSE for AD and with NfL for FTD. AD patients with higher deviations from the trend presented higher MMSE values. In FTD, lower NfL levels were associated with higher deviations from the CTh prediction. For AD and HC, we could predict longitudinal visits with the presented model trained with baseline data. For FTD, the longitudinal visits had more variability.

CONCLUSION

We highlight the value of CTh models for studying AD and FTD longitudinal changes and variability and their relationships with cognitive features and biomarkers.

Classifying Alzheimer's disease and frontotemporal dementia using machine learning with cross-sectional and longitudinal magnetic resonance imaging data

Alzheimer's disease (AD) and frontotemporal dementia (FTD) are common causes of dementia with partly overlapping, symptoms and brain signatures. There is a need to establish an accurate diagnosis and to obtain markers for disease tracking. We combined unsupervised and supervised machine learning to discriminate between AD and FTD using brain magnetic resonance imaging (MRI). We included baseline 3T-T1 MRI data from 339 subjects: 99 healthy controls (CTR), 153 AD and 87 FTD patients; and 2-year follow-up data from 114 subjects. We obtained subcortical gray matter volumes and cortical thickness measures using FreeSurfer. We used dimensionality reduction to obtain a single feature that was later used in a support vector machine for classification. Discrimination patterns were obtained with the contribution of each region to the single feature. Our algorithm differentiated CTR versus AD and CTR versus FTD at the cross-sectional level with 83.3% and 82.1% of accuracy. These increased up to 90.0% and 88.0% with longitudinal data. When we studied the classification between AD versus FTD we obtained an accuracy of 63.3% at the cross-sectional level and 75.0% for longitudinal data. The AD versus FTD versus CTR classification has reached an accuracy of 60.7%, and 71.3% for cross-sectional and longitudinal data respectively. Disease discrimination brain maps are in concordance with previous results obtained with classical approaches. By using a single feature, we were capable to classify CTR, AD, and FTD with good accuracy, considering the inherent overlap between diseases. Importantly, the algorithm can be used with cross-sectional and longitudinal data.

COVID-19 severity is related to poor executive function in people with post-COVID conditions

Patients with post-coronavirus disease 2019 (COVID-19) conditions typically experience cognitive problems. Some studies have linked COVID-19 severity with long-term cognitive damage, while others did not observe such associations. This discrepancy can be attributed to methodological and sample variations. We aimed to clarify the relationship between COVID-19 severity and long-term cognitive outcomes and determine whether the initial symptomatology can predict long-term cognitive problems. Cognitive evaluations were performed on 109 healthy controls and 319 post-COVID individuals categorized into three groups according to the WHO clinical progression scale: severe-critical (n = 77), moderate-hospitalized (n = 73), and outpatients (n = 169). Principal component analysis was used to identify factors associated with symptoms in the acute-phase and cognitive domains. Analyses of variance and regression linear models were used to study intergroup differences and the relationship between initial symptomatology and long-term cognitive problems. The severe-critical group performed significantly worse than the control group in general cognition (Montreal Cognitive Assessment), executive function (Digit symbol, Trail Making Test B, phonetic fluency), and social cognition (Reading the Mind in the Eyes test). Five components of symptoms emerged from the principal component analysis: the "Neurologic/Pain/Dermatologic" "Digestive/Headache", "Respiratory/Fever/Fatigue/Psychiatric" and "Smell/ Taste" components were predictors of Montreal Cognitive Assessment scores; the "Neurologic/Pain/Dermatologic" component predicted attention and working memory; the "Neurologic/Pain/Dermatologic" and "Respiratory/Fever/Fatigue/Psychiatric" components predicted verbal memory, and the "Respiratory/Fever/Fatigue/Psychiatric," "Neurologic/Pain/Dermatologic," and "Digestive/Headache" components predicted executive function. Patients with severe COVID-19 exhibited persistent deficits in executive function. Several initial symptoms were predictors of long-term sequelae, indicating the role of systemic inflammation and neuroinflammation in the acute-phase symptoms of COVID-19." Study Registration: www.ClinicalTrials.gov , identifier NCT05307549 and NCT05307575.

The APOE4 effect: structural brain differences in Alzheimer's disease according to the age at symptom onset

BACKGROUND AND PURPOSE

How the APOE genotype can differentially affect cortical and subcortical memory structures in biomarker-confirmed early-onset (EOAD) and late-onset (LOAD) Alzheimer's disease (AD) was assessed.

METHOD

Eighty-seven cerebrospinal fluid (CSF) biomarker-confirmed AD patients were classified according to their APOE genotype and age at onset. 28 were EOAD APOE4 carriers (+EOAD), 21 EOAD APOE4 non-carriers (-EOAD), 23 LOAD APOE4 carriers (+LOAD) and 15 LOAD APOE4 non-carriers (-LOAD). Grey matter (GM) volume differences were analyzed using voxel-based morphometry in Papez circuit regions. Multiple regression analyses were performed to determine the relation between GM volume loss and cognition.

RESULTS

Significantly more mammillary body atrophy in +EOAD compared to -EOAD is reported. The medial temporal and posterior cingulate cortex showed less GM in +LOAD compared to -LOAD. Medial temporal GM volume loss was also found in +EOAD compared to -LOAD. With an exception for +EOAD, medial temporal GM was strongly associated with episodic memory in the three groups, whilst posterior cingulate cortex GM volume was more related with visuospatial abilities. Visuospatial abilities and episodic memory were also associated with the anterior thalamic nucleus in -LOAD.

CONCLUSIONS

Our results show that the APOE genotype has a significant effect on GM integrity as a function of age of disease onset. Specifically, whilst LOAD APOE4 genotype is mostly associated with increased medial temporal and parietal atrophy compared to -LOAD, for EOAD APOE4 might have a more specific effect on subcortical (mammillary body) structures. The findings suggest that APOE genotype needs to be taken into account when classifying patients by age at onset.

Loss of brainstem white matter predicts onset and motor neuron symptoms in C9orf72 expansion carriers: a GENFI study

BACKGROUND AND OBJECTIVES

The C9orf72 expansion is the most common genetic cause of frontotemporal dementia (FTD) and/or motor neuron disease (MND). Corticospinal degeneration has been described in post-mortem neuropathological studies in these patients, especially in those with MND. We used MRI to analyze white matter (WM) volumes in presymptomatic and symptomatic C9orf72 expansion carriers and investigated whether its measure may be helpful in predicting the onset of symptoms.

METHODS

We studied 102 presymptomatic C9orf72 mutation carriers, 52 symptomatic carriers: 42 suffering from FTD and 11 from MND, and 75 non-carriers from the Genetic Frontotemporal dementia Initiative (GENFI). All subjects underwent T1-MRI acquisition. We used FreeSurfer to estimate the volume proportion of WM in the brainstem regions (midbrain, pons, and medulla oblongata). We calculated group differences with ANOVA tests and performed linear and non-linear regressions to assess group-by-age interactions.

RESULTS

A reduced WM ratio was found in all brainstem subregions in symptomatic carriers compared to both noncarriers and pre-symptomatic carriers. Within symptomatic carriers, MND patients presented a lower ratio in pons and medulla oblongata compared with FTD patients. No differences were found between presymptomatic carriers and non-carriers. Clinical severity was negatively associated with the WM ratio. C9orf72 carriers presented greater age-related WM loss than non-carriers, with MND patients showing significantly more atrophy in pons and medulla oblongata.

DISCUSSION

We find consistent brainstem WM loss in C9orf72 symptomatic carriers with differences related to the clinical phenotype supporting the use of brainstem measures as neuroimaging biomarkers for disease tracking.

Amplitudes of resting-state functional networks - investigation into their correlates and biophysical properties

Resting-state fMRI studies have shown that multiple functional networks, which consist of distributed brain regions that share synchronised spontaneous activity, co-exist in the brain. As these resting-state networks (RSNs) have been thought to reflect the brain's intrinsic functional organization, intersubject variability in the networks' spontaneous fluctuations may be associated with individuals' clinical, physiological, cognitive, and genetic traits. Here, we investigated resting-state fMRI data along with extensive clinical, lifestyle, and genetic data collected from 37,842 UK Biobank participants, with the object of elucidating intersubject variability in the fluctuation amplitudes of RSNs. Functional properties of the RSN amplitudes were first examined by analyzing correlations with the well-established between-network functional connectivity. It was found that a network amplitude is highly correlated with the mean strength of the functional connectivity that the network has with the other networks. Intersubject clustering analysis showed the amplitudes are most strongly correlated with age, cardiovascular factors, body composition, blood cell counts, lung function, and sex, with some differences in the correlation strengths between sensory and cognitive RSNs. Genome-wide association studies (GWASs) of RSN amplitudes identified several significant genetic variants reported in previous GWASs for their implications in sleep duration. We provide insight into key factors determining RSN amplitudes and demonstrate that intersubject variability of the amplitudes primarily originates from differences in temporal synchrony between functionally linked brain regions, rather than differences in the magnitude of raw voxelwise BOLD signal changes. This finding additionally revealed intriguing differences between sensory and cognitive RSNs with respect to sex effects on temporal synchrony and provided evidence suggesting that synchronous coactivations of functionally linked brain regions, and magnitudes of BOLD signal changes, may be related to different genetic mechanisms. These results underscore that intersubject variability of the amplitudes in health and disease need to be interpreted largely as a measure of the sum of within-network temporal synchrony and amplitudes of BOLD signals, with a dominant contribution from the former.

Neuropsychological impairment in post-COVID condition individuals with and without cognitive complaints

One of the most prevalent symptoms of post-COVID condition is cognitive impairment, which results in a significant degree of disability and low quality of life. In studies with large sample sizes, attention, memory, and executive function were reported as long-term cognitive symptoms. This study aims to describe cognitive dysfunction in large post-COVID condition individuals, compare objective neuropsychological performance in those post-COVID condition individuals with and without cognitive complaints, and identify short cognitive exams that can differentiate individuals with post-COVID symptoms from controls. To address these aims, the Nautilus project was started in June 2021. During the first year, we collected 428 participants' data, including 319 post-COVID and 109 healthy controls (18-65 years old) from those who underwent a comprehensive neuropsychological battery for cognitive assessment. Scores on tests assessing global cognition, learning and long-term memory, processing speed, language and executive functions were significantly worse in the post-COVID condition group than in healthy controls. Montreal Cognitive Assessment, digit symbol test, and phonetic verbal fluency were significant in the binomial logistic regression model and could effectively distinguish patients from controls with good overall sensitivity and accuracy. Neuropsychological test results did not differ between those with and without cognitive complaints. Our research suggests that patients with post-COVID conditions experience significant cognitive impairment and that routine tests like the Montreal Cognitive Assessment, digit symbol, and phonetic verbal fluency test might identify cognitive impairment. Thus, the administration of these tests would be helpful for all patients with post-COVID-19 symptoms, regardless of whether cognitive complaints are present or absent.

STUDY REGISTRATION

www.ClinicalTrials.gov, identifiers NCT05307549 and NCT05307575.

Evaluating the performance of Bayesian and frequentist approaches for longitudinal modeling: application to Alzheimer's disease

Linear mixed effects (LME) modelling under both frequentist and Bayesian frameworks can be used to study longitudinal trajectories. We studied the performance of both frameworks on different dataset configurations using hippocampal volumes from longitudinal MRI data across groups—healthy controls (HC), mild cognitive impairment (MCI) and Alzheimer’s disease (AD) patients, including subjects that converted from MCI to AD. We started from a big database of 1250 subjects from the Alzheimer’s disease neuroimaging initiative (ADNI), and we created different reduced datasets simulating real-life situations using a random-removal permutation-based approach. The number of subjects needed to differentiate groups and to detect conversion to AD was 147 and 115 respectively. The Bayesian approach allowed estimating the LME model even with very sparse databases, with high number of missing points, which was not possible with the frequentist approach. Our results indicate that the frequentist approach is computationally simpler, but it fails in modelling data with high number of missing values.

Parameters from site classification to harmonize MRI clinical studies: Application to a multi-site Parkinson's disease dataset

Multi‐site MRI datasets are crucial for big data research. However, neuroimaging studies must face the batch effect. Here, we propose an approach that uses the predictive probabilities provided by Gaussian processes (GPs) to harmonize clinical‐based studies. A multi‐site dataset of 216 Parkinson's disease (PD) patients and 87 healthy subjects (HS) was used. We performed a site GP classification using MRI data. The outcomes estimated from this classification, redefined like Weighted HARMonization PArameters (WHARMPA), were used as regressors in two different clinical studies: A PD versus HS machine learning classification using GP, and a VBM comparison (FWE‐p < .05, k = 100). Same studies were also conducted using conventional Boolean site covariates, and without information about site belonging. The results from site GP classification provided high scores, balanced accuracy (BAC) was 98.39% for grey matter images. PD versus HS classification performed better when the WHARMPA were used to harmonize (BAC = 78.60%; AUC = 0.90) than when using the Boolean site information (BAC = 56.31%; AUC = 0.71) and without it (BAC = 57.22%; AUC = 0.73). The VBM analysis harmonized using WHARMPA provided larger and more statistically robust clusters in regions previously reported in PD than when the Boolean site covariates or no corrections were added to the model. In conclusion, WHARMPA might encode global site‐effects quantitatively and allow the harmonization of data. This method is user‐friendly and provides a powerful solution, without complex implementations, to clean the analyses by removing variability associated with the differences between sites.

Longitudinal brain atrophy and CSF biomarkers in early-onset Alzheimer's disease

There is evidence of longitudinal atrophy in posterior brain areas in early-onset Alzheimer's disease (EOAD; aged < 65 years), but no studies have been conducted in an EOAD cohort with fluid biomarkers characterization. We used 3T-MRI and Freesurfer 6.0 to investigate cortical and subcortical gray matter loss at two years in 12 EOAD patients (A + T + N + ) compared to 19 controls (A-T-N-) from the Hospital Clínic Barcelona cohort. We explored group differences in atrophy patterns and we correlated atrophy and baseline CSF-biomarkers levels in EOAD. We replicated the correlation analyses in 14 EOAD (A + T + N + ) and 55 late-onset AD (LOAD; aged ≥ 75 years; A + T + N + ) participants from the Alzheimer's disease Neuroimaging Initiative. We found that EOAD longitudinal atrophy spread with a posterior-to-anterior gradient and beyond hippocampus/amygdala. In EOAD, higher initial CSF NfL levels correlated with higher ventricular volumes at baseline. On the other hand, higher initial CSF Aβ42 levels (within pathological range) predicted higher rates of cortical loss in EOAD. In EOAD and LOAD subjects, higher CSF t-tau values at baseline predicted higher rates of subcortical atrophy. CSF p-tau did not show any significant correlation. In conclusion, posterior cortices, hippocampus and amygdala capture EOAD atrophy from early stages. CSF Aβ42 might predict cortical thinning and t-tau/NfL subcortical atrophy.

tDCS-Induced Memory Reconsolidation Effects and Its Associations With Structural and Functional MRI Substrates in Subjective Cognitive Decline

Previous evidence suggests that transcranial direct current stimulation (tDCS) to the left dorsolateral prefrontal cortex (l-DLPFC) can enhance episodic memory in subjects with subjective cognitive decline (SCD), known to be at risk of dementia. Our main goal was to replicate such findings in an independent sample and elucidate if baseline magnetic resonance imaging (MRI) characteristics predicted putative memory improvement. Thirty-eight participants with SCD (aged: 60-65 years) were randomly assigned to receive active (N = 19) or sham (N = 19) tDCS in a double-blind design. They underwent a verbal learning task with 15 words (DAY-1), and 24 h later (DAY-2) stimulation was applied for 15 min at 1.5 mA targeting the l-DLPFC after offering a contextual reminder. Delayed recall and recognition were measured 1 day after the stimulation session (DAY-3), and at 1-month follow-up (DAY-30). Before the experimental session, structural and functional MRI were acquired. We identified a group∗time interaction in recognition memory, being the active tDCS group able to maintain stable memory performance between DAY-3 and DAY-30. MRI results revealed that individuals with superior tDCS-induced effects on memory reconsolidation exhibited higher left temporal lobe thickness and greater intrinsic FC within the default-mode network. Present findings confirm that tDCS, through the modulation of memory reconsolidation, is capable of enhancing performance in people with self-perceived cognitive complaints. Results suggest that SCD subjects with more preserved structural and functional integrity might benefit from these interventions, promoting maintenance of cognitive function in a population at risk to develop dementia.

Impaired Structural Connectivity in Parkinson's Disease Patients with Mild Cognitive Impairment: A Study Based on Probabilistic Tractography

Background: Probabilistic tractography, in combination with graph theory, has been used to reconstruct the structural whole-brain connectome. Threshold-free network-based statistics (TFNBS) is a useful technique to study structural connectivity in neurodegenerative disorders; however, there are no previous studies using TFNBS in Parkinson's disease (PD) with and without mild cognitive impairment (MCI). Materials and Methods: Sixty-two PD patients, 27 of whom classified as PD-MCI, and 51 healthy controls (HC) underwent diffusion-weighted 3T magnetic resonance imaging. Probabilistic tractography, using FMRIB Software Library (FSL), was used to compute the number of streamlines (NOS) between regions. NOS matrices were used to find group differences with TFNBS, and to calculate global and local measures of network integrity using graph theory. A binominal logistic regression was then used to assess the discrimination between PD with and without MCI using non-overlapping significant tracts. Tract-based spatial statistics were also performed with FSL to study changes in fractional anisotropy (FA) and mean diffusivity. Results: PD-MCI showed 37 white matter connections with reduced connectivity strength compared with HC, mainly involving temporal/occipital regions. These were able to differentiate PD-MCI from PD without MCI with an area under the curve of 83-85%. PD without MCI showed disrupted connectivity in 18 connections involving frontal/temporal regions. No significant differences were found in graph measures. Only PD-MCI showed reduced FA compared with HC. Discussion: TFNBS based on whole-brain probabilistic tractography can detect structural connectivity alterations in PD with and without MCI. Reduced structural connectivity in fronto-striatal and posterior cortico-cortical connections is associated with PD-MCI.

Functional brain changes associated with cognitive trajectories determine specific tDCS-induced effects among older adults

The combination of transcranial direct current stimulation (tDCS) with functional magnetic resonance imaging (fMRI) can provide original data to investigate age-related brain changes. We examined neural activity modulations induced by two multifocal tDCS procedures based on two distinct montages fitting two N-back task-based fMRI patterns ("compensatory" and "maintenance") related to high working memory (WM) in a previous publication (Fernández-Cabello et al. Neurobiol Aging (2016);48:23-33). We included 24 participants classified as stable or decliners according to their 4-year WM trajectories following a retrospective longitudinal approach. Then, we studied longitudinal fMRI differences between groups (stable and decliners) and across multifocal tDCS montages ("compensatory" and "maintenance") applied using a single-blind sham-controlled cross-over design. Decliners evidenced over-activation of non-related WM areas after 4 years of follow-up. Focusing on tDCS effects, among the decliner group, the "compensatory"-tDCS montage reduced the activity over the posterior regions where these subjects showed longitudinal hyperactivation. These results reinforce the notion that tDCS effects are characterized by an activity reduction and might be more noticeable in compromised systems. Importantly, the data provide novel evidence that cognitive trajectories predict tDCS effects in older adults.

Minimizing acquisition-related radiomics variability by image resampling and batch effect correction to allow for large-scale data analysis

OBJECTIVE

To identify CT-acquisition parameters accounting for radiomics variability and to develop a post-acquisition CT-image correction method to reduce variability and improve radiomics classification in both phantom and clinical applications.

METHODS

CT-acquisition protocols were prospectively tested in a phantom. The multi-centric retrospective clinical study included CT scans of patients with colorectal/renal cancer liver metastases. Ninety-three radiomics features of first order and texture were extracted. Intraclass correlation coefficients (ICCs) between CT-acquisition protocols were evaluated to define sources of variability. Voxel size, ComBat, and singular value decomposition (SVD) compensation methods were explored for reducing the radiomics variability. The number of robust features was compared before and after correction using two-proportion z test. The radiomics classification accuracy (K-means purity) was assessed before and after ComBat- and SVD-based correction.

RESULTS

Fifty-three acquisition protocols in 13 tissue densities were analyzed. Ninety-seven liver metastases from 43 patients with CT from two vendors were included. Pixel size, reconstruction slice spacing, convolution kernel, and acquisition slice thickness are relevant sources of radiomics variability with a percentage of robust features lower than 80%. Resampling to isometric voxels increased the number of robust features when images were acquired with different pixel sizes (p < 0.05). SVD-based for thickness correction and ComBat correction for thickness and combined thickness-kernel increased the number of reproducible features (p < 0.05). ComBat showed the highest improvement of radiomics-based classification in both the phantom and clinical applications (K-means purity 65.98 vs 73.20).

CONCLUSION

CT-image post-acquisition processing and radiomics normalization by means of batch effect correction allow for standardization of large-scale data analysis and improve the classification accuracy.

KEY POINTS

• The voxel size (accounting for the pixel size and slice spacing), slice thickness, and convolution kernel are relevant sources of CT-radiomics variability. • Voxel size resampling increased the mean percentage of robust CT-radiomics features from 59.50 to 89.25% when comparing CT scans acquired with different pixel sizes and from 71.62 to 82.58% when the scans were acquired with different slice spacings. • ComBat batch effect correction reduced the CT-radiomics variability secondary to the slice thickness and convolution kernel, improving the capacity of CT-radiomics to differentiate tissues (in the phantom application) and the primary tumor type from liver metastases (in the clinical application).

Disease-related cortical thinning in presymptomatic granulin mutation carriers

•

No differences in cortical thickness were found between presymptomatic GRN mutation carriers and non-carriers at the group-wise comparison.

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Presymptomatic GRN mutations carriers present distinct age-related CTh loss in frontal areas.

•

We do not fount influence of the TMEM106B genotype in the age-related CTh of GRN carriers.

Mutations in the granulin gene (GRN) cause familial frontotemporal dementia. Understanding the structural brain changes in presymptomatic GRN carriers would enforce the use of neuroimaging biomarkers for early diagnosis and monitoring. We studied 100 presymptomatic GRN mutation carriers and 94 noncarriers from the Genetic Frontotemporal dementia initiative (GENFI), with MRI structural images. We analyzed 3T MRI structural images using the FreeSurfer pipeline to calculate the whole brain cortical thickness (CTh) for each subject. We also perform a vertex-wise general linear model to assess differences between groups in the relationship between CTh and diverse covariables as gender, age, the estimated years to onset and education. We also explored differences according to TMEM106B genotype, a possible disease modifier. Whole brain CTh did not differ between carriers and noncarriers. Both groups showed age-related cortical thinning. The group-by-age interaction analysis showed that this age-related cortical thinning was significantly greater in GRN carriers in the left superior frontal cortex. TMEM106B did not significantly influence the age-related cortical thinning. Our results validate and expand previous findings suggesting an increased CTh loss associated with age and estimated proximity to symptoms onset in GRN carriers, even before the disease onset.

Effect of a 2-year diet intervention with walnuts on cognitive decline. The Walnuts And Healthy Aging (WAHA) study: a randomized controlled trial

BACKGROUND

Walnut consumption counteracts oxidative stress and inflammation, 2 drivers of cognitive decline. Clinical data concerning effects on cognition are lacking.

OBJECTIVES

The Walnuts And Healthy Aging study is a 2-center (Barcelona, Spain; Loma Linda, CA) randomized controlled trial examining the cognitive effects of a 2-y walnut intervention in cognitively healthy elders.

METHODS

We randomly allocated 708 free-living elders (63-79 y, 68% women) to a diet enriched with walnuts at ∼15% energy (30-60 g/d) or a control diet (abstention from walnuts). We administered a comprehensive neurocognitive test battery at baseline and 2 y. Change in the global cognition composite was the primary outcome. We performed repeated structural and functional brain MRI in 108 Barcelona participants.

RESULTS

A total of 636 participants completed the intervention. Besides differences in nutrient intake, participants from Barcelona smoked more, were less educated, and had lower baseline neuropsychological test scores than those from Loma Linda. Walnuts were well tolerated and compliance was good. Modified intention-to-treat analyses (n = 657) uncovered no between-group differences in the global cognitive composite, with mean changes of -0.072 (95% CI: -0.100, -0.043) in the walnut diet group and -0.086 (95% CI: -0.115, -0.057) in the control diet group (P = 0.491). Post hoc analyses revealed significant differences in the Barcelona cohort, with unadjusted changes of -0.037 (95% CI: -0.077, 0.002) in the walnut group and -0.097 (95% CI: -0.137, -0.057) in controls (P = 0.040). Results of brain fMRI in a subset of Barcelona participants indicated greater functional network recruitment in a working memory task in controls.

CONCLUSIONS

Walnut supplementation for 2 y had no effect on cognition in healthy elders. However, brain fMRI and post hoc analyses by site suggest that walnuts might delay cognitive decline in subgroups at higher risk. These encouraging but inconclusive results warrant further investigation, particularly targeting disadvantaged populations, in whom greatest benefit could be expected.This trial was registered at clinicaltrials.gov as NCT01634841.

Resting State Networks in the TgF344-AD Rat Model of Alzheimer's Disease Are Altered From Early Stages

A better and non-invasive characterization of the preclinical phases of Alzheimer's disease (AD) is important to advance its diagnosis and obtain more effective benefits from potential treatments. The TgF344-AD rat model has been well characterized and shows molecular, behavioral and brain connectivity alterations that resemble the silent period of the pathology. Our aim was to longitudinally investigate functional brain connectivity in established resting-state networks (RSNs) obtained by independent component analysis (ICA) in a cohort of TgF344-AD and control rats every 3 months, from 5 to 18 months of age, to cover different stages of the disease. Before each acquisition, working memory performance was evaluated by the delayed non match-to-sample (DNMS) task. Differences in the temporal evolution were observed between groups in the amplitude and shape of the somatosensorial and sensorimotor networks but not in the whole default mode network (DMN). Subsequent high dimensional ICA analysis showed early alterations in the anterior DMN subnetwork activity of TgF344-AD rats compared to controls. Performance of DNMS task was positively correlated with somatosensorial network at 5 months of age in the wild-type (WT) animals but not in the Tg-F344 rats. At different time points, DMN showed negative correlation with cognitive performance in the control group while in the transgenic group the correlation was positive. In addition, behavioral differences observed at 5 months of age correlated with alterations in the posterior DMN subnetwork. We have demonstrated that functional connectivity using ICA represents a useful biomarker also in animal models of AD such as the TgF344AD rats, as it allows the identification of alterations associated with the progression of the disease, detecting differences in specific networks even at very early stages.

Epileptogenic Zone Localization With 18FDG PET Using a New Dynamic Parametric Analysis

Introduction: [¹⁸F]fluorodeoxyglucose (¹⁸F-FDG) positron emission tomography (PET) is part of the regular preoperative work-up in medically refractory epilepsy. As a complement to visual evaluation of PET, statistical parametric maps can help in the detection of the epileptogenic zone (EZ). However, software packages currently available are time-consuming and little intuitive for physicians. We develop a user-friendly software (referred as PET-analysis) for EZ localization in PET studies that allows dynamic real-time statistical parametric analysis. To evaluate its performance, the outcome of PET-analysis was compared with the results obtained by visual assessment and Statistical Parametric Mapping (SPM). Methods: Thirty patients with medically refractory epilepsy who underwent presurgical ¹⁸F-FDG PET with good post-operative outcomes were included. The ¹⁸F-FDG PET studies were evaluated by visual assessment, with SPM8 and PET-analysis. In SPM, parametric T-maps were thresholded at corrected p < 0.05 and cluster size k = 50 and at uncorrected p < 0.001 and k = 100 (the most used parameters in the literature). Since PET-analysis rapidly processes different threshold combinations, T-maps were thresholded with multiple p-value and different clusters sizes. The presurgical EZ identified by visual assessment, SPM and PET-analysis was compared to the confirmed EZ according to post-surgical follow-up. Results: PET-analysis obtained 66.7% (20/30) of correctly localizing studies, comparable to the 70.0% (21/30) achieved by visual assessment and significantly higher (p < 0.05) than that obtained with the SPM threshold p < 0.001/k = 100, of 36.7% (11/30). Only one study was positive, albeit non-localizing, with the SPM threshold corrected p < 0.05/k = 50. Concordance was substantial for PET-analysis (κ = 0.643) and visual interpretation (κ = 0.622), being fair for SPM (κ = 0.242). Conclusion: Compared to SPM with the fixed standard parameters, PET-analysis may be superior in EZ localization with its easy and rapid processing of different threshold combinations. The results of this initial proof-of-concept study validate the clinical use of PET-analysis as a robust objective complementary tool to visual assessment for EZ localization.

Spatial parcellations, spectral filtering, and connectivity measures in fMRI: Optimizing for discrimination

The analysis of Functional Connectivity (FC) is a key technique of fMRI, having been used to distinguish brain states and conditions. While many approaches to calculating FC are available, there have been few assessments of their differences, making it difficult to choose approaches, and compare results. Here, we assess the impact of methodological choices on discriminability, using a fully controlled data set of continuous active states involving basic visual and motor tasks, providing robust localized FC changes. We tested a range of anatomical and functional parcellations, including the AAL atlas, parcellations derived from the Human Connectome Project and Independent Component Analysis (ICA) of many dimensionalities. We measure amplitude, covariance, correlation, and regularized partial correlation under different temporal filtering choices. We evaluate features derived from these methods for discriminating states using MVPA. We find that multidimensional parcellations derived from functional data performed similarly, outperforming an anatomical atlas, with correlation and partial correlation (p < .05, FDR). Partial correlation, with appropriate regularization, outperformed correlation. Amplitude and covariance generally discriminated less well, although gave good results with high‐dimensionality ICA. We found that discriminative FC properties are frequency specific; higher frequencies performed surprisingly well under certain configurations of atlas choices and dependency measures, with ICA‐based parcellations revealing greater discriminability at high frequencies compared to other parcellations. Methodological choices in FC analyses can have a profound impact on results and can be selected to optimize accuracy, interpretability, and sharing of results. This work contributes to a basis for consistent selection of approaches to estimating and analyzing FC.

Translational Challenge Models in Support of Efficacy Studies: Neurobehavioral and Cognitive Changes Induced by Transcranial Magnetic Stimulation in Healthy Volunteers

Transcranial Magnetic Stimulation (TMS) was proposed as a neurophysiological tool almost three decades ago. It now encompasses a very wide range of applications including clinical research and the treatment of psychiatric, neurologic and medical conditions such as depression, schizophrenia, addictions, post-traumatic stress disorders, pain, migraine, stroke, Alzheimer's disease, autism, multiple sclerosis and Parkinson's disease. By inducing electrical brain responses through the administration of magnetic pulses, TMS is in a unique position to painlessly modulate cortical regions and offers good spatial resolution and excellent temporal resolution, particularly when applied using single pulses. However, despite the impressive number of papers describing the use of TMS to modulate cognitive functions, the mechanisms underlying the behavioral changes observed after stimulation have not been fully identified. Here we present a review of the ability of TMS to transiently compromise brain function in humans. The primary aim was to investigate its capacity for use as a 'cognitive challenge model' in human pharmacological studies. The data reviewed include findings on executive function, attention and episodic memory. For each cognitive process, the convergent and divergent results are discussed in terms of paradigm differences and in order to define the optimal methodology for obtaining the desired effects.

White Matter Abnormalities Track Disease Progression in PSEN1 Autosomal Dominant Alzheimer's Disease

PSEN1 mutations are the most frequent cause of autosomal dominant Alzheimer's disease (ADAD), and show nearly full penetrance. There is presently increasing interest in the study of biomarkers that track disease progression in order to test therapeutic interventions in ADAD. We used white mater (WM) volumetric characteristics and diffusion tensor imaging (DTI) metrics to investigate correlations with the normalized time to expected symptoms onset (relative age ratio) and group differences in a cohort of 36 subjects from PSEN1 ADAD families: 22 mutation carriers, 10 symptomatic (SMC) and 12 asymptomatic (AMC), and 14 non-carriers (NC). Subjects underwent a 3T MRI. WM morphometric data and DTI metrics were analyzed. We found that PSEN1 MC showed significant negative correlation between fractional anisotropy (FA) and the relative age ratio in the genus and body of corpus callosum and corona radiate (p <  0.05 Family-wise error correction (FWE) at cluster level) and positive correlation with mean diffusivity (MD), axial diffusivity (AxD), and radial diffusivity (RD) in the splenium of corpus callosum. SMC presented WM volume loss, reduced FA and increased MD, AxD, and RD in the anterior and posterior corona radiate, corpus callosum (p <  0.05 FWE) compared with NC. No significant differences were observed between AMC and NC in WM volume or DTI measures. These findings suggest that the integrity of the WM deteriorates linearly in PSEN1 ADAD from the early phases of the disease; thus DTI metrics might be useful to monitor the disease progression. However, the lack of significant alterations at the preclinical stages suggests that these indexes might not be good candidates for early markers of the disease.

CSF neurofilament light levels predict hippocampal atrophy in cognitively healthy older adults

Cerebrospinal fluid (CSF) neurofilament light (NFL) is a marker of axonal degeneration. We tested whether CSF NFL levels predict hippocampal atrophy rate in cognitively healthy older adults independently of the established CSF Alzheimer's disease (AD) biomarkers, β-amyloid 1-42, and phosphorylated tau (P-tau). We included 144 participants in a 2-year longitudinal study with baseline CSF measures and 2 magnetic resonance images. Eighty-eight participants had full data available. A subgroup of 36 participants with very low AD risk was also studied. NFL predicted hippocampal atrophy rate independently of age, β-amyloid 1-42, and P-tau. Including NFL, P-tau, and age in the same model, higher NFL and lower P-tau predicted higher hippocampal atrophy (R² = 0.20, NFL: β = -0.34; p = 0.003; P-tau: β = 0.27; p = 0.009). The results were upheld in the participants with very low AD risk. NFL predicted neurodegeneration in older adults with very low AD probability. We suggest that factors previously shown to be important for brain degeneration in mild cognitive impairment may also impact changes in normal aging, demonstrating that NFL is likely to indicate AD-independent, age-expected neurodegeneration.

Rich Club Organization and Cognitive Performance in Healthy Older Participants

The human brain is a complex network that has been noted to contain a group of densely interconnected hub regions. With a putative “rich club” of hubs hypothesized to play a central role in global integrative brain functioning, we assessed whether hub and rich club organizations are associated with cognitive performance in healthy participants and whether the rich club might be differentially involved in cognitive functions with a heavier dependence on global integration. A group of 30 relatively older participants (range = 39–79 years of age) underwent extensive neuropsychological testing, combined with diffusion-weighted magnetic resonance imaging to reconstruct individual structural brain networks. Rich club connectivity was found to be associated with general cognitive performance. More specifically, assessing the relationship between the rich club and performance in two specific cognitive domains, we found rich club connectivity to be differentially associated with attention/executive functions—known to rely on the integration of distributed brain areas—rather than with visuospatial/visuoperceptual functions, which have a more constrained neuroanatomical substrate. Our findings thus provide first empirical evidence of a relevant role played by the rich club in cognitive processes.

Functional networks and structural connectivity of visuospatial and visuoperceptual working memory

Neural correlates of working memory (WM) in healthy subjects have been extensively investigated using functional MRI (fMRI). However it still remains unclear how cortical areas forming part of functional WM networks are also connected by white matter fiber bundles, and whether DTI measures, used as indices of microstructural properties and directionality of these connections, can predict individual differences in task performance. fMRI data were obtained from 23 healthy young subjects while performing one visuospatial (square location) and one visuoperceptual (face identification) 2-back task. Diffusion tensor imaging (DTI) data were also acquired. We used independent component analysis (ICA) of fMRI data to identify the main functional networks involved in WM tasks. Voxel-wise DTI analyses were performed to find correlations between structural white matter and task performance measures, and probabilistic tracking of DTI data was used to identify the white matter bundles connecting the nodes of the functional networks. We found that functional recruitment of the fusiform and the inferior frontal cortex was specific for the visuoperceptual working memory task, while there was a high overlap in brain activity maps in parietal and middle frontal areas for both tasks. Axial diffusivity and fractional anisotropy, of the tracts connecting the fusiform with the inferior frontal areas correlated with processing speed in the visuoperceptual working memory task. Although our findings need to be considered as exploratory, we conclude that both tasks share a highly-overlapping pattern of activity in areas of frontal and parietal lobes with the only differences in activation between tasks located in the fusiform and inferior frontal regions for the visuoperceptual task. Moreover, we have found that the DTI measures are predictive of the processing speed.

Reorganization of brain networks in aging: a review of functional connectivity studies

Healthy aging (HA) is associated with certain declines in cognitive functions, even in individuals that are free of any process of degenerative illness. Functional magnetic resonance imaging (fMRI) has been widely used in order to link this age-related cognitive decline with patterns of altered brain function. A consistent finding in the fMRI literature is that healthy old adults present higher activity levels in some brain regions during the performance of cognitive tasks. This finding is usually interpreted as a compensatory mechanism. More recent approaches have focused on the study of functional connectivity, mainly derived from resting state fMRI, and have concluded that the higher levels of activity coexist with disrupted connectivity. In this review, we aim to provide a state-of-the-art description of the usefulness and the interpretations of functional brain connectivity in the context of HA. We first give a background that includes some basic aspects and methodological issues regarding functional connectivity. We summarize the main findings and the cognitive models that have been derived from task-activity studies, and we then review the findings provided by resting-state functional connectivity in HA. Finally, we suggest some future directions in this field of research. A common finding of the studies included is that older subjects present reduced functional connectivity compared to young adults. This reduced connectivity affects the main brain networks and explains age-related cognitive alterations. Remarkably, the default mode network appears as a highly compromised system in HA. Overall, the scenario given by both activity and connectivity studies also suggests that the trajectory of changes during task may differ from those observed during resting-state. We propose that the use of complex modeling approaches studying effective connectivity may help to understand context-dependent functional reorganizations in the aging process.

Evolving brain structural changes in PSEN1 mutation carriers

Familial Alzheimer's disease provides the opportunity to investigate brain changes even before the symptoms onset. We performed a structural magnetic resonance imaging (MRI) study in 38 participants from families with presenilin 1 gene mutations: 11 symptomatic mutation carriers, 13 asymptomatic mutation carriers (AMC), with a mean of 16.22 years before the estimated appearance of symptoms, and 14 noncarriers. A subset of subjects was studied longitudinally 2 and 4 years after the first scan. We found decreased cortical thickness (CTh) and volume in cortical and subcortical structures in symptomatic mutation carriers, with progressive loss over time. In AMC, we found increased CTh and volume in temporoparietal regions and in precuneus-posterior cingulate compared with controls at baseline. Longitudinal studies in AMC, by contrast, showed accelerated rates of CTh loss in precuneus-posterior cingulate and superior parietal, right lateral temporal and left orbitofrontal, and middle frontal regions. These findings suggest that brain structure in presenilin 1 mutation carriers follows nonlinear trajectories, with regional increases during the very early presymptomatic period. Initial neuroinflammation and/or accumulation of amyloid species followed by neurodegeneration, or congenital morphometric differences, may explain the observed features.

Decreased Default Mode Network connectivity correlates with age-associated structural and cognitive changes

Ageing entails cognitive and motor decline as well as brain changes such as loss of gray (GM) and white matter (WM) integrity, neurovascular and functional connectivity alterations. Regarding connectivity, reduced resting-state fMRI connectivity between anterior and posterior nodes of the Default Mode Network (DMN) relates to cognitive function and has been postulated to be a hallmark of ageing. However, the relationship between age-related connectivity changes and other neuroimaging-based measures in ageing is fragmentarily investigated. In a sample of 116 healthy elders we aimed to study the relationship between antero-posterior DMN connectivity and measures of WM integrity, GM integrity and cerebral blood flow (CBF), assessed with an arterial spin labeling sequence. First, we replicated previous findings demonstrating DMN connectivity decreases in ageing and an association between antero-posterior DMN connectivity and memory scores. The results showed that the functional connectivity between posterior midline structures and the medial prefrontal cortex was related to measures of WM and GM integrity but not to CBF. Gray and WM correlates of anterio-posterior DMN connectivity included, but were not limited to, DMN areas and cingulum bundle. These results resembled patterns of age-related vulnerability which was studied by comparing the correlates of antero-posterior DMN with age-effect maps. These age-effect maps were obtained after performing an independent analysis with a second sample including both young and old subjects. We argue that antero-posterior connectivity might be a sensitive measure of brain ageing over the brain. By using a comprehensive approach, the results provide valuable knowledge that may shed further light on DMN connectivity dysfunctions in ageing.

Cognitive impairment and resting-state network connectivity in Parkinson's disease

The purpose of this work was to evaluate changes in the connectivity patterns of a set of cognitively relevant, dynamically interrelated brain networks in association with cognitive deficits in Parkinson's disease (PD) using resting-state functional MRI. Sixty-five nondemented PD patients and 36 matched healthy controls were included. Thirty-four percent of PD patients were classified as having mild cognitive impairment (MCI) based on performance in attention/executive, visuospatial/visuoperceptual (VS/VP) and memory functions. A data-driven approach using independent component analysis (ICA) was used to identify the default-mode network (DMN), the dorsal attention network (DAN) and the bilateral frontoparietal networks (FPN), which were compared between groups using a dual-regression approach controlling for gray matter atrophy. Additional seed-based analyses using a priori defined regions of interest were used to characterize local changes in intranetwork and internetwork connectivity. Structural group comparisons through voxel-based morphometry and cortical thickness were additionally performed to assess associated gray matter atrophy. ICA results revealed reduced connectivity between the DAN and right frontoinsular regions in MCI patients, associated with worse performance in attention/executive functions. The DMN displayed increased connectivity with medial and lateral occipito-parietal regions in MCI patients, associated with worse VS/VP performance, and with occipital reductions in cortical thickness. In line with data-driven results, seed-based analyses mainly revealed reduced within-DAN, within-DMN and DAN-FPN connectivity, as well as loss of normal DAN-DMN anticorrelation in MCI patients. Our findings demonstrate differential connectivity changes affecting the networks evaluated, which we hypothesize to be related to the pathophysiological bases of different types of cognitive impairment in PD.

Functional brain networks and cognitive deficits in Parkinson's disease

Graph-theoretical analyses of functional networks obtained with resting-state functional magnetic resonance imaging (fMRI) have recently proven to be a useful approach for the study of the substrates underlying cognitive deficits in different diseases. We used this technique to investigate whether cognitive deficits in Parkinson's disease (PD) are associated with changes in global and local network measures. Thirty-six healthy controls (HC) and 66 PD patients matched for age, sex, and education were classified as having mild cognitive impairment (MCI) or not based on performance in the three mainly affected cognitive domains in PD: attention/executive, visuospatial/visuoperceptual (VS/VP), and declarative memory. Resting-state fMRI and graph theory analyses were used to evaluate network measures. We have found that patients with MCI had connectivity reductions predominantly affecting long-range connections as well as increased local interconnectedness manifested as higher measures of clustering, small-worldness, and modularity. The latter measures also tended to correlate negatively with cognitive performance in VS/VP and memory functions. Hub structure was also reorganized: normal hubs displayed reduced centrality and degree in MCI PD patients. Our study indicates that the topological properties of brain networks are changed in PD patients with cognitive deficits. Our findings provide novel data regarding the functional substrate of cognitive impairment in PD, which may prove to have value as a prognostic marker.

Task-dependent activity and connectivity predict episodic memory network-based responses to brain stimulation in healthy aging

BACKGROUND

Transcranial magnetic stimulation (TMS) can affect episodic memory, one of the main cognitive hallmarks of aging, but the mechanisms of action remain unclear.

OBJECTIVES

To evaluate the behavioral and functional impact of excitatory TMS in a group of healthy elders.

METHODS

We applied a paradigm of repetitive TMS - intermittent theta-burst stimulation - over left inferior frontal gyrus in healthy elders (n = 24) and evaluated its impact on the performance of an episodic memory task with two levels of processing and the associated brain activity as captured by a pre and post fMRI scans.

RESULTS

In the post-TMS fMRI we found TMS-related activity increases in left prefrontal and cerebellum-occipital areas specifically during deep encoding but not during shallow encoding or at rest. Furthermore, we found a task-dependent change in connectivity during the encoding task between cerebellum-occipital areas and the TMS-targeted left inferior frontal region. This connectivity change correlated with the TMS effects over brain networks.

CONCLUSIONS

The results suggest that the aged brain responds to brain stimulation in a state-dependent manner as engaged by different tasks components and that TMS effect is related to inter-individual connectivity changes measures. These findings reveal fundamental insights into brain network dynamics in aging and the capacity to probe them with combined behavioral and stimulation approaches.

Evolving brain functional abnormalities in PSEN1 mutation carriers: a resting and visual encoding fMRI study

PSEN1 mutations are the most frequent cause of familial Alzheimer's disease and show nearly full penetrance. Here we studied alterations in brain function in a cohort of 19 PSEN1 mutation carriers: 8 symptomatic (SMC) and 11 asymptomatic (AMC). Asymptomatic carriers were, on average, 12 years younger than the predicted age of disease onset. Thirteen healthy subjects were used as a control group (CTR). Subjects underwent a 10-min resting-state functional magnetic resonance imaging (fMRI) scan and also performed a visual encoding task. The analysis of resting-state fMRI data revealed alterations in the default mode network, with increased frontal connectivity and reduced posterior connectivity in AMC and decreased frontal and increased posterior connectivity in SMC. During task-related fMRI, SMC showed reduced activity in regions of the left occipital and left prefrontal cortices, while both AMC and SMC showed increased activity in a region within the precuneus/posterior cingulate, all as compared to CTR. Our findings suggest that fMRI can detect evolving changes in brain mechanisms in PSEN1 mutation carriers and support the use of this technique as a biomarker in Alzheimer's disease, even before the appearance of clinical symptoms.

White matter/gray matter contrast changes in chronic and diffuse traumatic brain injury

Signal-intensity contrast of T1-weighted magnetic resonance imaging scans has been associated with tissue integrity and reported as a sign of neurodegenerative changes in diseases such as Alzheimer's disease. After severe traumatic brain injury (TBI), progressive structural changes occur in white (WM) and gray matter (GM). In the current study, we assessed the signal-intensity contrast of GM and WM in patients with diffuse TBI in the chronic stage to (1) characterize the regional pattern of WM/GM changes in intensity contrast associated with traumatic axonal injury, (2) evaluate possible associations between this measure and diffusion tensor image (DTI)/fractional anisotropy (FA) for detecting WM damage, and (3) investigate the correlates of both measures with cognitive outcomes. Structural T1 scans were processed with FreeSurfer software to identify the boundary and calculate the WM/GM contrast maps. DTIs were processed with the FMRIB software library to obtain FA maps. The WM/GM contrast in TBI patients showed a pattern of reduction in almost all of the brain, except the visual and motor primary regions. Global FA values obtained from DTI correlated with the intensity contrast of all associative cerebral regions. WM/GM contrast correlated with memory functions, whereas FA global values correlated with tests measuring memory and mental processing speed. In conclusion, tissue-contrast intensity is a very sensitive measure for detecting structural brain damage in chronic, severe and diffuse TBI, but is less sensitive than FA for reflecting neuropsychological sequelae, such as impaired mental processing speed.

Resting-state functional magnetic resonance imaging activity and connectivity and cognitive outcome in traumatic brain injury

IMPORTANCE

The study of brain activity and connectivity at rest provides a unique opportunity for the investigation of the brain substrates of cognitive outcome after traumatic axonal injury. This knowledge may contribute to improve clinical management and rehabilitation programs.

OBJECTIVE

To study functional magnetic resonance imaging abnormalities in signal amplitude and brain connectivity at rest and their relationship to cognitive outcome in patients with chronic and severe traumatic axonal injury.

DESIGN

Observational study.

SETTING

University of Barcelona and Hospital Clinic de Barcelona, Barcelona, and Institut Guttmann-Neurorehabilitation Hospital, Badalona, Spain.

PARTICIPANTS

Twenty patients with traumatic brain injury (TBI) were studied, along with 17 matched healthy volunteers.

INTERVENTIONS

Resting-state functional magnetic resonance imaging and diffusion tensor imaging data were acquired. After exploring group differences in amplitude of low-frequency fluctuations (ALFF), we studied functional connectivity within the default mode network (DMN) by means of independent component analysis, followed by a dual regression approach and seed-based connectivity analyses. Finally, we performed probabilistic tractography between the frontal and posterior nodes of the DMN.

MAIN OUTCOMES AND MEASURES

Signal amplitude and functional connectivity during the resting state, tractography related to DMN, and the association between signal amplitudes and cognitive outcome.

RESULTS

Patients had greater ALFF in frontal regions, which was correlated with cognitive performance. Within the DMN, patients showed increased connectivity in the frontal lobes. Seed-based connectivity analyses revealed augmented connectivity within surrounding areas of the frontal and left parietal nodes of the DMN. Fractional anisotropy of the cingulate tract was correlated with increased connectivity of the frontal node of the DMN in patients with TBI.

CONCLUSIONS AND RELEVANCE

Increased ALFF is related to better cognitive performance in chronic TBI. The loss of structural connectivity produced by damage to the cingulum tract explained the compensatory increases in functional connectivity within the frontal node of the DMN.

Progressive changes in a recognition memory network in Parkinson's disease

BACKGROUND

In a previous functional MRI (fMRI) study, we found that patients with Parkinson's disease (PD) presented with dysfunctions in the recruitment of recognition memory networks. We aimed to investigate the changes in these networks over time.

METHODS

We studied 17 PD patients and 13 age and sex matched healthy subjects. In both groups fMRI (recognition memory paradigm) and neuropsychological assessments were obtained at baseline and at follow-up. To analyse changes over time in functional networks, model free (independent component analysis) analyses of the fMRI data were carried out. Then, a cross correlation approach was used to assess the changes in the strength of functional connectivity.

RESULTS

At follow-up, patients showed reduced recruitment of one network, including decreased activation in the orbitofrontal cortices, middle frontal gyri, frontal poles, anterior paracingulate cortex, superior parietal lobes and left middle temporal gyrus, as well as decreased deactivation in the anterior paracingulate gyrus and precuneus. Cross correlation analyses over time showed a decrease in the strength of functional connectivity between the middle frontal gyrus and the superior parietal lobe in PD patients.

CONCLUSIONS

Model free fMRI and cross correlation connectivity analyses were able to detect progressive changes in functional networks involved in recognition memory in PD patients at early disease stages and without overt clinical deterioration. Functional connectivity analyses could be useful to monitor changes in brain networks underlying neuropsychological deficits in PD.

Distinctive age-related temporal cortical thinning in asymptomatic granulin gene mutation carriers

Studies in asymptomatic granulin gene (GRN) mutation carriers are essential to improve our understanding of the pattern and timing of early morphologic brain changes in frontotemporal lobar degeneration. The main objectives of this study were to assess the effect of age in cortical thickness changes (CTh) in preclinical GRN mutation carriers and to study the relationship of CTh with cognitive performance in GRN mutation carriers. We calculated CTh maps in 13 asymptomatic carriers of the c.709-1G>A GRN mutation and 13 age- and sex-matched healthy subjects. Asymptomatic GRN mutation carriers presented different patterns of age-related cortical thinning in the right superior temporal and middle temporal gyri and the banks of the superior temporal sulcus bilaterally when compared with controls. Cortical thickness was correlated with neuropsychological test scores: Trail Making Tests A and B, and the Boston Naming Test. Distinctive age-related cortical thinning in asymptomatic GRN mutation carriers in lateral temporal cortices suggests an early and disease-specific effect in these areas.