Episodic memory and learning rates in amyotrophic lateral sclerosis without dementia.

In amyotrophic lateral sclerosis (ALS), memory deficits may be primary or secondary to executive dysfunction. We assessed episodic memory and executive function of nondemented ALS patients, comparing episodic memory profiles and learning rates of ALS patients with those of mild cognitive impairment (MCI) subjects and cognitively healthy controls (HC). In a multidisciplinary tertiary centre for motor neuron disease, 72 nondemented ALS patients, 57 amnestic MCI (aMCI), 89 single non amnestic MCI with compromised executive functions (dysexecutive MCI), and 190 HC were enrolled. They were screened using the Frontal Assessment Battery and Mini Mental State Examination. Episodic memory performances and learning rates were tested using the Rey Auditory Verbal Learning Test (RAVLT). Episodic memory dysfunction (immediate recall) was found in 14 ALS patients (19.4%). The ALS group had lower performance than HC on immediate recall, without differences in learning rate, and better performance than aMCI subjects on all RAVLT measures. Compared to dysexecutive MCI subjects, ALS patients had only better verbal learning abilities. ALS patients with executive dysfunction had a lower score on immediate and delayed recalls, verbal learning, and primacy effect than ALS patients without executive dysfunction. The immediate recall among couples of diagnostic groups differed in a statistically significant way except for the ALS/dysexecutive MCI groups. In ALS patients, episodic memory performances and learning rates appeared to be better than in aMCI subjects and similar to those with dysexecutive MCI, suggesting also a secondary functional damage due to executive impairment.

Clinical and genetic analyses of familial and sporadic frontotemporal dementia patients in Southern Italy.

INTRODUCTION: We investigated the clinical differences between familial and sporadic frontotemporal dementia (FTD), screening for mutations in known FTD genes. METHODS: We diagnosed 22 affected individuals belonging to eight families and 43 sporadic cases with FTD in Apulia, Southern Italy, in 2 years. Mutations in common causative FTD genes (GRN, MAPT, VCP, and TARDBP) and C9ORF72 expansions were screened. RESULTS: Behavioral variant of FTD was the most common clinical subtype (50% and 69% in familial and sporadic cases, respectively). Social conduct impairment/disinhibition, loss of insight, and inflexibility were the most frequent clinical features observed at onset. One new mutation was identified in GRN in family A. DISCUSSION: Disease onset in sporadic FTD was more frequently characterized by a clustering of behavioral symptoms with apathy and loss of personal hygiene. Mutations in common causative FTD genes are not a major cause of familial and sporadic FTD in the Southern Italian population.

A Novel Splice-Acceptor Site Mutation in GRN (c.709-2 A>T) Causes Frontotemporal Dementia Spectrum in a Large Family from Southern Italy.

Heterozygous loss of function mutations in granulin represent a significant cause of frontotemporal lobar degeneration with ubiquitin and TDP-43 inclusions (FTLD-TDP). We report a novel GRN splice site mutation (c.709-2 A>T), segregating with frontotemporal dementia spectrum in a large family from southern Italy. The GRN c.709-2 A>T is predicted to result in the skipping of exon 8, leading to non-sense mediated mRNA decay. Moreover, the PGRN plasma levels in the GRN c.709-2 A>T carriers were significantly lower (24 ng/ml) compared to controls (142.7 ng/ml) or family members non-carriers (82.0 ng/ml) (p-value = 0.005, Kruskal Wallis), suggesting progranulin haploinsufficiency. We do not report any potential pathogenic GRN mutation in a follow-up cohort composed of 6 FTD families and 43 sporadic FTD cases, from the same geographic area. Our study suggests that GRN (c.709-2 A>T) is a novel and likely very rare cause of FTD in this Italian cohort. Finally, in line with previous studies, we show that GRN haploinsufficiency leads to a heterogeneous clinical picture, and plasma progranulin levels may be a reliable tool to identify GRN loss of function mutations. However, given that a) genetic and environmental factors, gender, and age may regulate PGRN plasma levels and b) plasma progranulin levels may not reflect PGRN levels in the central nervous system, we suggest that the measurement of progranulin in the plasma should always be coupled with genetic screening of GRN for mutations.

Classification of Healthy Subjects and Alzheimer's Disease Patients with Dementia from Cortical Sources of Resting State EEG Rhythms: A Study Using Artificial Neural Networks.

Previous evidence showed a 75.5% best accuracy in the classification of 120 Alzheimer's disease (AD) patients with dementia and 100 matched normal elderly (Nold) subjects based on cortical source current density and linear lagged connectivity estimated by eLORETA freeware from resting state eyes-closed electroencephalographic (rsEEG) rhythms (Babiloni et al., 2016a). Specifically, that accuracy was reached using the ratio between occipital delta and alpha1 current density for a linear univariate classifier (receiver operating characteristic curves). Here we tested an innovative approach based on an artificial neural network (ANN) classifier from the same database of rsEEG markers. Frequency bands of interest were delta (2-4 Hz), theta (4-8 Hz Hz), alpha1 (8-10.5 Hz), and alpha2 (10.5-13 Hz). ANN classification showed an accuracy of 77% using the most 4 discriminative rsEEG markers of source current density (parietal theta/alpha 1, temporal theta/alpha 1, occipital theta/alpha 1, and occipital delta/alpha 1). It also showed an accuracy of 72% using the most 4 discriminative rsEEG markers of source lagged linear connectivity (inter-hemispherical occipital delta/alpha 2, intra-hemispherical right parietal-limbic alpha 1, intra-hemispherical left occipital-temporal theta/alpha 1, intra-hemispherical right occipital-temporal theta/alpha 1). With these 8 markers combined, an accuracy of at least 76% was reached. Interestingly, this accuracy based on 8 (linear) rsEEG markers as inputs to ANN was similar to that obtained with a single rsEEG marker (Babiloni et al., 2016a), thus unveiling their information redundancy for classification purposes. In future AD studies, inputs to ANNs should include other classes of independent linear (i.e., directed transfer function) and non-linear (i.e., entropy) rsEEG markers to improve the classification.