A delay spread cancelling waveform characterizer for RF power amplifiers

A two channel 65 nm CMOS RF-waveform characterizer is presented that enables multi-harmonic Adaptive Matching Networks (AMN) or Adaptive Digital Pre-Distortion (ADPD) in RF-power amplifiers. The characterizer measures the DC component and the first 3 harmonics of RF signals by applying a DFT to 8 (ideally) equally spaced quasi-DC output voltages. Conventionally in these types of systems accuracy is limited by sample timing accuracies, which in our case are mainly due to delay cell mismatch. We introduce a novel way to cancel delay cell mismatch, that significantly increases measurement accuracy at the cost of only a small power and area increase. The RF-waveform characterizer achieves 6.8-bit measurement linearity together with a (clock feedthrough limited) 24 dB SFDR. The measured power consumption for our proof-of-principle demonstrator is 18.6 mW at a maximum input signal frequency of 1.1 GHz under continuous operation

Longitudinal Network Changes and Conversion to Cognitive Impairment in Multiple Sclerosis

OBJECTIVE: To characterize functional network changes related to conversion to cognitive impairment in a large sample of MS patients over a period of 5 years. METHODS: 227 MS patients and 59 healthy controls (HCs) of the Amsterdam MS cohort underwent neuropsychological testing and resting-state fMRI at two time points (time-interval 4.9±0.9 years). At both baseline and follow-up, patients were categorized as cognitively preserved (CP, N=123), mildly impaired (MCI, Z<-1.5 on ≥2 cognitive tests, N=32) or impaired (CI, Z<-2 on ≥2 tests, N=72) and longitudinal conversion between groups was determined. Network function was quantified using eigenvector centrality, a measure of regional network importance, which was computed for individual resting-state networks at both time-points. RESULTS: Over time, 18.9% of patients converted to a worse phenotype; 22/123 CP patients (17.9%) converted from CP to MCI, 10/123 from CP to CI (8.1%) and 12/32 MCI patients converted to CI (37.5%). At baseline, DMN centrality was higher in CI compared to controls (P=.05). Longitudinally, ventral attention network (VAN) importance increased in CP, driven by stable CP and CP-to-MCI converters (P<.05). CONCLUSIONS: Of all patients, 19% worsened in their cognitive status over five years. Conversion from intact cognition to impairment is related to an initial disturbed functioning of the VAN, then shifting towards DMN dysfunction in CI. As the VAN normally relays information to the DMN, these results could indicate that in MS, normal processes crucial for maintaining overall network stability are progressively disrupted as patients clinically progress

Accurate differentiation between physiological and pathological ripples recorded with scalp-EEG

OBJECTIVE: To compare scalp-EEG recorded physiological ripples co-occurring with vertex waves to pathological ripples co-occurring with interictal epileptiform discharges (IEDs). METHODS: We marked ripples in sleep EEGs of children. We compared the start of ripples to vertex wave- or IED-start, and duration, frequency, and root mean square (RMS) amplitude of physiological and pathological ripples using multilevel modeling. Ripples were classified as physiological or pathological using linear discriminant analysis. RESULTS: We included 40 children with and without epilepsy. Ripples started (χ2(1) = 38.59, p < 0.001) later if they co-occurred with vertex waves (108.2 ms after vertex wave-start) than if they co-occurred with IEDs (4.3 ms after IED-start). Physiological ripples had longer durations (75.7 ms vs 53.0 ms), lower frequencies (98.3 Hz vs 130.6 Hz), and lower RMS amplitudes (0.9 μV vs 1.8 μV, all p < 0.001) than pathological ripples. Ripples could be classified as physiological or pathological with 98 % accuracy. Ripples recorded in children with idiopathic or symptomatic epilepsy seemed to form two subgroups of pathological ripples. CONCLUSIONS: Ripples co-occurring with vertex waves or IEDs have different characteristics and can be differentiated as physiological or pathological with high accuracy. SIGNIFICANCE: This is the first study that compares physiological and pathological ripples recorded with scalp EEG