Background and Aims:

We aimed to assess cortical, subcortical and cerebellar grey matter (GM) atrophy using MRI in patients with disorders of the frontotemporal lobar degeneration (FTLD) spectrum with known genetic mutations.

Methods:

Sixty-six patients carrying FTLD-related mutations were enrolled, including 44 with pure motor neuron disease (MND) and 22 with frontotemporal dementia (FTD). Sixty-one patients with sporadic FTLD (sFTLD) matched for age, sex and disease severity with genetic FTLD (gFTLD) were also included, as well as 52 healthy controls. Voxel-based morphometry (VBM) was performed. GM volumes of subcortical and cerebellar structures were obtained.

Results:

Compared with controls, GM atrophy on VBM was greatest in genetic FTD, whereas sporadic MND (sMND) patients showed focal motor cortical atrophy. Patients carrying C9ORF72 and GRN mutations showed the most widespread cortical volume loss, in contrast with GM sparing in SOD1 and TARDBP. Globally, gFTLD patients showed greater atrophy of parietal cortices and thalami compared with sFTLD. In volumetric analysis, gFTLD patients showed volume loss compared with sFTLD in the caudate nuclei and thalami, especially comparing C9-MND with sMND. In the cerebellum, gFTLD patients showed greater atrophy of the right lobule VIIb than sFTLD. Thalamic volumes of gFTLD patients with a C9ORF72 mutation showed an inverse correlation with Frontal Behavioral Inventory scores.

Conclusions:

Measures of deep GM and cerebellar structural involvement may be useful markers of gFTLD, particularly C9ORF72-related disorders, regardless of the clinical presentation within the FTLD spectrum.

Study funding: Italian Ministry of Health (RF-2011-02351193; GR-2011-02351217; GR-2013-02357415) and the European Research Council (StG-2016_714388_NeuroTRACK).

Background and Aims:

To investigate cortical/subcortical and network functional alterations in PD patients with freezing of gait (PD-FoG), PD developing FoG (PD-FoG-converters) and PD not developing FoG (PD-non-converters) over one and two years.

Methods:

Thirty PD-FoG, 11 PD-FoG-converters and 11 PD-non-converters, age, sex, education, disease duration and severity matched, were followed for two years. Thirty age, sex and education matched healthy controls were included at baseline. Participants underwent clinical/MRI visits to evaluate cortical thickness, basal ganglia volumes and functional graph metrics at baseline and their changes over two years. Correlations between baseline MRIs and clinical worsening in PD groups and a ROC curve to investigate if any MRI measure at baseline could differentiate PD-FoG-converters and non-converters were run.

Results:

At baseline, PD-FoG had widespread cortical/subcortical atrophy, while PD-FoG-converters and non-converters showed atrophy in sensorimotor areas and basal ganglia. PD-FoG-converters relative to controls and PD-FoG showed higher global and parietal local efficiency and clustering coefficient. Over time, PD-FoG showed posterior cingulate atrophy but stable functional graph metrics. PD-FoG-converters accumulated occipital atrophy and reduced parietal clustering coefficient, while PD-non-converters showed fronto-parietal and temporal atrophy and increased sensorimotor path length. Both structural and functional baseline MRI alterations correlated with worse executive/attentive functions over time in PD-FoG. Higher parietal clustering coefficient at baseline differentiated PD-FoG–converters from PD-non-converters.

Conclusions:

Structural MRI is a useful tool to monitor PD progression, while functional MRI may be helpful to identify FoG conversion early.

Funding. Ministry of Education, Science, and Technological Development of the Republic of Serbia (#175090).

Background and Aims:

The presymptomatic brain changes of granulin (GRN) disease, preceding by years frontotemporal dementia, need to be still fully characterized. Dynamic functional network connectivity (dFNC) allows to capture both spatial networks configurations and their dynamic changes over time.

Aim of the study is to investigate the dFNC in 141 presymptomatic GRN mutation carriers and 282 non-carriers from the Genetic Frontotemporal dementia research Initiative (GENFI) cohort.

Methods:

We considered time-varying patterns of the default mode network, the language network and the salience network, each presenting in four distinct recurring spatial states. Dwell time (DT), i.e., the time each individual spends in each spatial state of each network, was considered. Correlations between DTs and estimated years from expected symptom onset (EYO) and clinical performances were assessed.

Results:

Presymptomatic GRN mutation carriers spent significantly more time in those spatial states characterised by greater activation of the insula and the parietal cortices, as compared to non-carriers (p<0.05, FDR-corrected). A significant correlation between DTs of these spatial states and EYO was found, the longer the time spent in the spatial states, the closer the EYO. DTs significantly correlated with performances at tests tapping processing speed, with worse scores associated with increased spatial states’ DTs.

Conclusions:

Our results demonstrated that presymptomatic GRN disease presents a complex dynamic re-organization of brain connectivity. dFNC, evaluating both the spatial and temporal changes of brain network connectivity, provides a unique glimpse into brain function and allows a more sophisticated evaluation of the earliest disease changes and the understanding of possible compensative mechanisms in GRN disease.

Background and Aims:

Psychogenic nonepileptic seizures (PNES) represent a multifactorial psychopathology, which makes diagnosis particularly challenging: PNES can be misdiagnosed as pharmaco-resistant temporal lobe epilepsy (TLE), and approximately 80% of subjects actually undergo anti-epileptic drug (AED) at the time of correct diagnosis. In this study, we used machine learning (ML) to differentiate PNES and TLE patients.

Methods:

Thirty-six PNES subjects and 43 demographically-matched TLE patients underwent neuropsychiatric/neuroimaging assessment. A 10,000-trees random forest (RF) algorithm, considered more robust to overfitting compared to other ML algorithms, was trained on T1-weighted MRI, i.e., on the entire set of morphological metrics obtained through FreeSurfer (cortical thickness, surface, volume, curvature, gyrification index). All features with a mean decrease in Gini index ≥ 0.30 were selected to construct a new classifier with the lowest out-of-bag error (OOB; accuracy = 100 - OOB).

Results:

Figure 1 shows the most important features discriminating PNES from TLE, ranked according to mean Gini Index decrease. This discriminant network included regions across all lobes of the brain, from parietal-occipital regions to frontal regions, as well as the anterior portion of the corpus callosum. Based on these selected features, the RF algorithm was able to distinguish PNES from TLE with an average accuracy of 77.2%.

Conclusions:

This work provides evidence that ML techniques could aid the differential diagnosis of PNES. Involvement of cingulate and orbitofrontal cortices, a frequent finding when comparing PNES to controls, also represented a distinctive feature from TLE patients. This finding supports the hypothesis that PNES subjects experience disrupted processing of emotional information, which might ultimately lead to the insurgence of seizure-like episodes.

Background and Aims:

Standardized MRI guidelines published in 2015 by the MAGNIMS group and in 2016 by the CMSC are important for the diagnosis and monitoring of multiple sclerosis (MS) patients. We present the international consensus 2021 revisions of the guidelines on MRI in MS, which merge recommendations from MAGNIMS, CMSC, and NAIMS.

Methods:

Two panels of experts convened to update existing guidelines. One panel convened in Graz, Austria to update MAGNIMS guidelines. A second panel met separately and independently in Newark, USA to update CMSC guidelines and discuss advocacy efforts. The leadership of the MAGNIMS, NAIMS, and CMSC working groups combined their efforts to reach an international consensus.

Results:

The revised guidelines on MRI in MS merges recommendations from MAGNIMS, CMSC, and NAIMS to improve the use of MRI for diagnosis, prognosis and monitoring of MS patients. 3D acquisitions are emphasized for optimal comparison over time. Core brain sequences include a 3D-T2wFLAIR for lesion identification and monitoring treatment effectiveness. Gadolinium-based contrast is recommended for diagnostic studies and judicious use for routine monitoring of MS patients. Additional DWI sequences are recommended for PML safety monitoring. Optional sequences that could be incorporated into future recommendations include high resolution 3D-T1w for brain atrophy monitoring; DIR/PSIR for identifying cortical lesions; and SWI for the central vein sign evaluation.

Conclusions:

Dissemination of the 2021 evidence-based MAGNIMS-CMSC-NAIMS international consensus guidelines through congresses would be a welcome addition to the advocacy efforts in promoting the use of standardized MRI in diagnosis and follow-up of MS patients.

Background and Aims:

Parkinson’s disease (PD) patients are classified as tremor-dominant (TD) and postural instability and gait disorder (PIGD) phenotypes. The aim of this study was to investigate functional alterations within motor circuits of the cerebro-cerebellar system in PD-TD and PD-PIGD groups using stepwise functional connectivity (SFC) method.

Methods:

32 PD-TD and 26 PD-PIGD patients performed clinical/cognitive evaluations and resting-state functional MRI (fMRI). 60 age- and sex-matched controls were also enrolled. SFC analysis aims to characterize regions that connect to specific seed brain areas at different levels of link-step distances. The cerebellar seed-region was identified using motor task in 23 controls. For each of the SFC maps, two-sample t-test comparisons between groups were performed.

Results:

The performance of the fMRI-motor task was associated with activation of the lobule VI and vermis of the cerebellum. SFC analysis at one-link step distance showed, in both PD subtypes, a decreased connectivity between seed-region and thalamus and parietal lobe relative to controls; across intermediate link-steps, a reduced connectivity was observed with frontal, parietal and occipital lobes. Only PD-PIGD patients showed lower connectivity at intermediate link-step distances between the seed-cerebellar region and sensorimotor areas. Moreover, SFC pattern identified different localization of functional over‐connectivity in frontal lobe in both PD groups: inferior frontal gyrus and insula in PD-PIGD, and in orbitofrontal gyrus in PD-TD.

Conclusions:

These findings highlight subtype-specific PD changes in cerebellar functional connectivity, providing novel insights into the pathophysiological mechanism underlying different motor phenotypes.

Funding: Ministry of Education and Science Republic of Serbia (#175090), Italian Ministry of Health (#RF-2018-12366746).

Background and Aims:

The loss of dopaminergic neurons in the ventral tegmental area (VTA) has been recently recognised as an early pathophysiological event of Alzheimer’s disease (AD). Using resting-state fMRI (RS-fMRI), we aimed here to investigate longitudinally, in a group of patients with mild cognitive impairment (MCI) due to AD, the impact of VTA-driven brain disconnection in predicting the conversion to AD.

Methods:

a cohort of 35 MCI patients were recruited and followed-up for 24 months. They underwent cognitive evaluation and MR scanning at 3T in two occasions, at baseline and at follow-up. RS-fMRI data were analysed to obtain in each subject VTA-driven connectivity maps to be used in group analyses.

Results:

At 24-month follow-up, 46% of patients converted to AD. Although MCI-converters and non-converter did not differ in demographic and behavioral characteristics at baseline, the former group showed already a significant reduction of VTA-driven connectivity in the posterior cingulate and precentral cortex. The patterns of connectivity observed at baseline remained substantially unchanged in both groups, MCI converters and nonconverters, when comparing them on fMRI data collected at follow-up. Moreover, a discriminant analysis showed that baseline VTA-disconnection together with hippocampal atrophy, correctly classify patients as converters and non-converter with high sensitivity, specificity and accuracy (all parameters above 68%).

Conclusions:

This study indicates a substantial contribution of dopaminergic dysfunction to AD progression since early clinical stages, by targeting the default-mode network. These results have implications for patient stratification in pharmacological and non-pharmacological clinical trials.