A multi-component, adaptive Working Memory Assessment Battery (WoMAB): validation and norms in an Italian population sample.

BACKGROUND: Working memory (WM) abilities are frequently impaired in neurological disorders affecting fronto-parietal cortical/sub-cortical structures. WM deficits negatively influence interventional outcomes and everyday functioning. This study thus aimed at the following: (a) developing and standardizing an ecologically valid task for WM assessment ( Ice Cream Test, ICT); (b) validating and norming a novel WM test (Digit Ordering Test, DOT), as well as providing updated norms for digit span (DS) tasks, in an Italian population sample; (c) introducing a novel scoring procedure for measuring WM. METHODS: One-hundred and sixty-eight Italian healthy participants-73 male, 95 females; age: 48.4 ± 19.1 (18-86); education: 12.1 ± 4.8 (4-21)-underwent a thorough WM assessment-DOT, ICT, and both forward and backward DS tasks (FDS, BDS). The ICT requires participants to act as waiters who have to keep track of customers' orders. For each task, WM and total (T) outcomes were computed, i.e., the number of elements in the longest sequence and that of recalled sequences, respectively. Norms were derived via the equivalent score (ES) method. RESULTS: DS ratios (DSRs) were computed for both WM/S and T outcomes on raw DS measures (BDS divided by FDS). Age and education significantly predicted all WM tasks; sex affected FDS and DSR-T scores (males > females). WM measures were highly internally related. DISCUSSION: The present work provides Italian practitioners with a normatively updated, multi-component, adaptive battery for WM assessment (WoMAB) as well as with novel outcomes which capture different WM facets-WM capacity and attentive monitoring abilities.

Brand new norms for a good old test: Northern Italy normative study of MiniMental State Examination.

AIM: Mini-Mental State Examination (MMSE) is one of the most used tests for the screening of global cognition in patients with neurological and medical disorders. Norms for the Italian version of the test were published in the 90 s; more recent norms were published in 2020 for Southern Italy only. In the present study, we computed novel adjustment coefficients, equivalent scores and cut-off value for Northern Italy (Lombardia and Veneto) and Italian speaking Switzerland. METHODS: We recruited 361 healthy young and old (range: 20-95 years) individuals of both sexes (men: 156, women: 205) and from different educational levels (range: 4-22 years). Neuropsychiatric disorders and severe medical conditions were excluded with a questionnaire and cognitive deficits and were ruled out with standardized neuropsychological tests assessing the main cognitive domains. We used a slightly modified version of MMSE: the word 'fiore' was replaced with 'pane' in verbal recalls to reduce the common interference error 'casa, cane, gatto'. The effect of socio-demographic features on performance at MMSE was assessed via multiple linear regression, with test raw score as dependent variable and sex, logarithm of 101-age and square root of schooling as predictors. RESULTS: Mean raw MMSE score was 28.8 ± 1.7 (range: 23-30). Multiple linear regression showed a significant effect of all socio-demographic variables and reported a value of R2 = 0.26. The new cut off was ≥ 26 /30. CONCLUSION: We provide here updated norms for a putatively more accurate version of Italian MMSE, produced in a Northern population but potentially valid all over Italy.

Sleep Power Topography in Children with Attention Deficit Hyperactivity Disorder (ADHD).

OBJECTIVE: Recent years saw an increasing interest towards sleep microstructure abnormalities in attention-deficit/hyperactivity disorder (ADHD). However, the existing literature on sleep electroencephalographic (EEG) power in ADHD is still controversial, often based on single electrode recordings, and mainly focused on slow wave activity (SWA) during NREM sleep. This study aimed to systematically investigate sleep power topography in all traditional frequency bands, in all sleep stages and across sleep cycles using high-density EEG (HD-EEG). METHOD: Thirty drug-naïve children with ADHD (10.5 ± 2.1 years, 21 male) and 23 typically developing (TD) control participants (mean age: 10.2 ± 1.6 years, 13 male) were included in the current analysis. Signal power topography was computed in classical frequency bands during sleep, contrasted between groups and sleep cycles, and correlated with measures of ADHD severity, cognitive functioning and estimated total sleep time. RESULTS: Compared to TD subjects, patients with ADHD consistently displayed a widespread increase in low-frequency activity (between 3 and 10 Hz) during NREM sleep, but not during REM sleep and wake before sleep onset. Such a difference involved a wide centro-posterior cluster of channels in the upper SWA range, in Theta, and low-Alpha. Between-group difference was maximal in sleep stage N3 in the first sleep cycle, and positively correlated with average total sleep time. CONCLUSIONS: These results support the concept that children with ADHD, compared to TD peers, have a higher sleep pressure and altered sleep homeostasis, which possibly interfere with (and delay) cortical maturation.

Shooting a high-density electroencephalographic picture on sleep in children with attention-deficit/hyperactivity disorder.

STUDY OBJECTIVES: Sleep-related slow-wave activity (SWA) has been recognized as a marker of synaptic plasticity. In children affected by attention deficit hyperactivity disorder (ADHD), SWA is mainly located in the central rather than frontal regions, reflecting a maturational delay. A detailed subjective and objective sleep investigation, including a full night video-polysomnography (PSG-HD-EEG), was performed on 30 consecutive drug naïve outpatients with a diagnosis of ADHD. They received a diagnosis of sleep disorders in 29/30 cases, and most of them had a past history of sleep problems. They had a higher apnea-hypopnea index at PSG, and slept less than 9 hr at actigraphy. We aimed to describe the SWA behavior in the same group of children with ADHD. MATERIALS AND METHODS: The full-night PSG-HD EEG of children with ADHD was compared with the one of the 25 healthy controls. The scalp SWA mapping, the decrease of SWA during the night, and the EEG source of SWA were analyzed. RESULTS: At scalp topography, the focus of SWA was observed over the centro-parietal-occipital regions in participants with ADHD (p < 0.01), which remained significant in the subgroups divided between subgroups according to the sleep diagnosis (p < 0.01). The physiological decrease in SWA was more evident in control participants. The source analysis revealed a greater delta power over the posterior cingulate in participants with ADHD (p < 0.01). CONCLUSIONS: Our results confirm static and dynamic changes in SWA behavior in children with ADHD, which may reflect a maturational delay occurring at a vulnerable age, as a consequence of chronic sleep deprivation.

Sleep phenotypes in attention deficit hyperactivity disorder.

OBJECTIVE: A case-control study was performed to test the hypothesis that children with attention deficit hyperactivity disorder (ADHD) have chronic sleep deprivation and may be classified into specific sleep-related phenotypes. METHODS: Thirty outpatients with ADHD (nine females, mean age 10.1 ± 2.1 years) were recruited consecutively, and given a comprehensive sleep assessment, including blood exams, sleep questionnaires, laboratory video-polysomnographic recordings (v-PSG), multiple sleep latency tests, and one-week actigraphy. The PSG parameters were compared to those of 25 age-matched controls (12 females, mean age 10.34 ± 1.54 years) who underwent only the v-PSG. RESULTS: ADHD children were classified as follows: a narcolepsy-like phenotype was found in four; delayed sleep onset insomnia in five; obstructive sleep apnea (OSA) in 15; periodic limb movements in eight, and sleep epileptiform discharges in 10 children. All subjects had a total sleep time shorter than 9 h at actigraphy, ferritin levels lower than 60 mcg/L, and a history of sleep problems (mainly OSA and insomnia). Compared to controls, the ADHD group had a higher apnea-hypopnea index at PSG. CONCLUSIONS: A full sleep assessment in children with ADHD confirmed the validity of the sleep phenotypes hypothesis, and revealed a much higher percentage of sleep problems than that found in the literature. Beyond the sleep phenotypes, all children reported a history of sleep problems and slept less than 9 h per night, indicating chronic sleep deprivation that should be evaluated as a possible unifying marker of ADHD.

Sleep-Related Disorders in Children with Attention-Deficit Hyperactivity Disorder: Preliminary Results of a Full Sleep Assessment Study.

BACKGROUND AND METHODS: We present the preliminary results of a prospective case-control sleep study in children with a diagnosis of attention-deficit hyperactivity disorder (ADHD). A deep sleep assessment including sleep questionnaires, sleep habits, a video-polysomnographic recording with full high-density electroencephalography (EEG) and cardiorespiratory polygraphy, multiple sleep latency test, and 1-week actigraphic recording were performed to verify whether children with ADHD may be classified into one of the following five phenotypes: (1) hypoarousal state, resembling narcolepsy, which may be considered a "primary" form of ADHD; (2) delayed sleep onset insomnia; (3) sleep-disordered breathing; (4) restless legs syndrome and/or periodic limb movements; and (5) sleep epilepsy and/or EEG interictal epileptiform discharges. RESULTS: Fifteen consecutive outpatients with ADHD were recruited (two female, mean age 10.6 ± 2.2, age range 8-13.7 years) over 6 months. The narcolepsy-like sleep phenotype was observed in three children, the sleep onset insomnia phenotype was observed in one child, mild obstructive sleep apnea was observed in three children, sleep hyperkinesia and/or PLMs were observed in five children, while IEDs and or nocturnal epilepsy were observed in three children. Depending on the sleep phenotype, children received melatonin, iron supplementation, antiepileptic drugs, or stimulants. CONCLUSIONS: Our study further highlights the need to design an efficient sleep diagnostic algorithm for children with ADHD, thereby more accurately identifying cases in which a full sleep assessment is indicated.