Structural and functional connectivity between the lateral posterior-pulvinar complex and primary visual cortex in the ferret

Altered functional-structural coupling may predict Parkinson's patient's depression

We aimed to elucidate the neurobiological basis of depression in Parkinson's disease and identify potential imaging markers for depression in patients with Parkinson's disease. We recruited 43 normal controls (NC), 46 depressed Parkinson's disease patients (DPD) and 56 non-depressed Parkinson's disease (NDPD). All participants underwent routine T2-weighted, T2Flair, and resting-state scans on the same 3.0 T magnetic resonance imaging (MRI) scanner at our hospital. Pre-processing includes calculating surface-based Regional Homogeneity (2DReHo) and cortical thickness. Then we defined the correlation coefficient between 2DReHo and cortical thickness as the functional-structural coupling index. Between-group comparisons were conducted on the Fisher's Z-transformed correlation coefficients. To identify specific regions of decoupling, the 2DReHo for each participant were divided by cortical thickness at each vertex, followed by threshold-free cluster enhancement (TFCE) multiple comparison correction. Binary logistic regression analysis was performed with DPD as the dependent variable, and significantly altered indicators as the independent variables. Receiver operating characteristic curves were constructed to compare the diagnostic performance of individual predictors and combinations using R and MedCalc software. DPD patients exhibited a significantly lower whole-brain functional-structural coupling index than NDPD patients and NC. Abnormal functional-structural coupling was primarily observed in the left inferior parietal lobule and right primary and early visual cortices in DPD patients. Receiver operating characteristic analysis revealed that the combination of cortical functional-structural coupling, surface-based ReHo, and thickness had the best diagnostic performance, achieving a sensitivity of 65% and specificity of 77.7%. This is the first study to explore the relationship between functional and structural changes in DPD patients and evaluate the diagnostic performance of these altered correlations to predict depression in Parkinson's disease patients. We posit that these changes in functional-structural relationships may serve as imaging biomarkers for depression in Parkinson's disease patients, potentially aiding in the classification and diagnosis of Parkinson's disease. Additionally, our findings provide functional and structural imaging evidence for exploring the neurobiological basis of depression in Parkinson's disease.

Causal oscillations in the visual thalamo-cortical network in sustained attention in ferrets

Sustained visual attention allows us to process and react to unpredictable, behaviorally relevant sensory input. Sustained attention engages communication between the higher-order visual thalamus and its connected cortical regions. However, it remains unclear whether there is a causal relationship between oscillatory circuit dynamics and attentional behavior in these thalamo-cortical circuits. By using rhythmic optogenetic stimulation in the ferret, we provide causal evidence that higher-order visual thalamus coordinates thalamo-cortical and cortico-cortical functional connectivity during sustained attention via spike-field phase locking. Increasing theta but not alpha power in the thalamus improved accuracy and reduced omission rates in a sustained attention task. Further, the enhancement of effective connectivity by stimulation was correlated with improved behavioral performance. Our work demonstrates a potential circuit-level causal mechanism for how the higher-order visual thalamus modulates cortical communication through rhythmic synchronization during sustained attention.

Sleep disturbance-related neuroimaging features as potential biomarkers for the diagnosis of major depressive disorder: A multicenter study based on machine learning

BACKGROUND

Objective biomarkers are crucial for overcoming the clinical dilemma in major depressive disorder (MDD), and the individualized diagnosis is essential to facilitate the precise medicine for MDD.

METHODS

Sleep disturbance-related magnetic resonance imaging (MRI) features was identified in the internal dataset (92 MDD patients) using the relevance vector regression algorithm, which was further verified in 460 MDD patients of an independent, multicenter dataset. Subsequently, using these MRI features, the eXtreme Gradient Boosting classification model was constructed in the current multicenter dataset (460 MDD patients and 470 normal controls). Meanwhile, the association between classification outputs and the severity of depressive symptoms was also investigated.

RESULTS

In MDD patients, the combination of gray matter density and fractional amplitude of low-frequency fluctuation can accurately predict individual sleep disturbance score that was calculated by the sum of item 4 score, item 5 score, and item 6 score of the 17-Item Hamilton Rating Scale for Depression (HAMD-17) (R² = 0.158 in the internal dataset; R² = 0.110 in multicenter dataset). Furthermore, the classification model based on these MRI features distinguished MDD patients from normal controls with 86.3% accuracy (area under the curve = 0.937). Importantly, the classification outputs significantly correlated with HAMD-17 scores in MDD patients.

LIMITATION

Lacking some specialized tools to assess the personal sleep quality, e.g. Pittsburgh Sleep Quality Index.

CONCLUSION

Neuroimaging features can reflect accurately individual sleep disturbance manifestation and serve as potential diagnostic biomarkers of MDD.

Multivariate Machine Learning Analyses in Identification of Major Depressive Disorder Using Resting-State Functional Connectivity: A Multicentral Study

Diagnosis of major depressive disorder (MDD) using resting-state functional connectivity (rs-FC) data faces many challenges, such as the high dimensionality, small samples, and individual difference. To assess the clinical value of rs-FC in MDD and identify the potential rs-FC machine learning (ML) model for the individualized diagnosis of MDD, based on the rs-FC data, a progressive three-step ML analysis was performed, including six different ML algorithms and two dimension reduction methods, to investigate the classification performance of ML model in a multicentral, large sample dataset [1021 MDD patients and 1100 normal controls (NCs)]. Furthermore, the linear least-squares fitted regression model was used to assess the relationships between rs-FC features and the severity of clinical symptoms in MDD patients. Among used ML methods, the rs-FC model constructed by the eXtreme Gradient Boosting (XGBoost) method showed the optimal classification performance for distinguishing MDD patients from NCs at the individual level (accuracy = 0.728, sensitivity = 0.720, specificity = 0.739, area under the curve = 0.831). Meanwhile, identified rs-FCs by the XGBoost model were primarily distributed within and between the default mode network, limbic network, and visual network. More importantly, the 17 item individual Hamilton Depression Scale scores of MDD patients can be accurately predicted using rs-FC features identified by the XGBoost model (adjusted R² = 0.180, root mean squared error = 0.946). The XGBoost model using rs-FCs showed the optimal classification performance between MDD patients and HCs, with the good generalization and neuroscientifical interpretability.

Task-related functional magnetic resonance imaging-based neuronavigation for the treatment of depression by individualized repetitive transcranial magnetic stimulation of the visual cortex

To determine whether repetitive transcranial magnetic stimulation (rTMS) of the visual cortex (VC) provides effective and well-tolerated treatment and whether magnetic resonance imaging (MRI) measures functional change of the VC as a biomarker of therapeutic effect in major depressive disorder (MDD), we performed a sham-controlled, double-blind, randomized, three-arm VC rTMS treatment study in 74 MDD patients. Neuronavigated rTMS (10 Hz, 90% of resting motor threshold, 1,600 pulses over 20 min twice per day) was performed over the VC for five days. Clinical outcome was measured by Hamilton Depression Rating Scale (HAMD-24) at days 0, 1, 3, 5 and after terminating rTMS, with follow-up at four weeks. MRI was measured at days 0 and 5. The individualized group exhibited the greatest change in HAMD-24 scores after VC rTMS for 5 days (F=5.53, P=0.005), which were maintained during follow-up period (F=4.22, P=0.016). All patients reported good tolerance. Changes in VC task-related functional MRI correlated with symptomatic reduction in the individualized group. Treatment reduced the initially abnormal increase in resting state functional connectivity from the VC to the pre/subgenual anterior cingulate cortex at day 5, especially in the individualized group. We demonstrated therapeutic potential and good tolerance of VC rTMS in MDD patients, indicated by biomarkers of fMRI measurement.

Thalamus exhibits less sensory variability quenching than cortex

Spiking activity exhibits a large degree of variability across identical trials, which has been shown to be significantly reduced by stimulus onset in a wide range of cortical areas. Whether similar dynamics apply to the thalamus and in particular to the pulvinar is largely unknown. Here, we examined electrophysiological recordings from two adult rhesus macaques performing a perceptual task and comparatively investigated trial-to-trial variability in higher-order thalamus (ventral and dorsal pulvinar), the lateral geniculate nucleus (LGN) and visual cortex (area V4) prior to and following the presentation of a visual stimulus. We found spiking variability during stable fixation prior to stimulus onset to be considerably lower in both pulvinar and the LGN as compared to area V4. In contrast to the prominent variability reduction in V4 upon stimulus onset, variability in the thalamic nuclei was largely unaffected by visual stimulation. There was a small but significant variability decrease in the dorsal pulvinar, but not in the ventral portion of the pulvinar, which is closely connected to visual cortices and would thus have been expected to reflect cortical response properties. This dissociation did not stem from differences in response strength or mean firing rates and indicates fundamental differences in variability quenching between thalamus and cortex.

Top-down, contextual entrainment of neuronal oscillations in the auditory thalamocortical circuit

Our results indicate that nonhuman primates detect complex repeating acoustic sequences in a continuous auditory stream, which is an important precursor for human speech learning and perception. We demonstrate that oscillatory entrainment, known to support the attentive perception of rhythmic stimulus sequences, can occur for rhythms defined solely by stimulus context rather than physical boundaries. As opposed to acoustically driven entrainment by rhythmic tone sequences demonstrated previously, this form of entrainment relies on the brain’s ability to group auditory inputs based on their statistical regularities. The internally initiated, context-driven modulation of excitability in the medial pulvinar prior to A1 supports the notion of top-down entrainment.

Prior studies have shown that repetitive presentation of acoustic stimuli results in an alignment of ongoing neuronal oscillations to the sequence rhythm via oscillatory entrainment by external cues. Our study aimed to explore the neural correlates of the perceptual parsing and grouping of complex repeating auditory patterns that occur based solely on statistical regularities, or context. Human psychophysical studies suggest that the recognition of novel auditory patterns amid a continuous auditory stimulus sequence occurs automatically halfway through the first repetition. We hypothesized that once repeating patterns were detected by the brain, internal rhythms would become entrained, demarcating the temporal structure of these repetitions despite lacking external cues defining pattern on- or offsets. To examine the neural correlates of pattern perception, neuroelectric activity of primary auditory cortex (A1) and thalamic nuclei was recorded while nonhuman primates passively listened to streams of rapidly presented pure tones and bandpass noise bursts. At arbitrary intervals, random acoustic patterns composed of 11 stimuli were repeated five times without any perturbance of the constant stimulus flow. We found significant delta entrainment by these patterns in the A1, medial geniculate body, and medial pulvinar. In A1 and pulvinar, we observed a statistically significant, pattern structure-aligned modulation of neuronal firing that occurred earliest in the pulvinar, supporting the idea that grouping and detecting complex auditory patterns is a top-down, context-driven process. Besides electrophysiological measures, a pattern-related modulation of pupil diameter verified that, like humans, nonhuman primates consciously detect complex repetitive patterns that lack physical boundaries.

Morphological heterogeneity among corticogeniculate neurons in ferrets: quantification and comparison with a previous report in macaque monkeys

The corticogeniculate (CG) pathway links the visual cortex with the lateral geniculate nucleus (LGN) of the thalamus and is the first feedback connection in the mammalian visual system. Whether functional connections between CG neurons and LGN relay neurons obey or ignore the separation of feedforward visual signals into parallel processing streams is not known. Accordingly, there is some debate about whether CG neurons are morphologically heterogeneous or homogenous. Here we characterized the morphology of CG neurons in the ferret, a visual carnivore with distinct feedforward parallel processing streams, and compared the morphology of ferret CG neurons with CG neuronal morphology previously described in macaque monkeys [Briggs et al. (2016) Neuron, 90, 388]. We used a G-deleted rabies virus as a retrograde tracer to label CG neurons in adult ferrets. We then reconstructed complete dendritic morphologies for a large sample of virus-labeled CG neurons. Quantification of CG morphology revealed three distinct CG neuronal subtypes with striking similarities to the CG neuronal subtypes observed in macaques. These findings suggest that CG neurons may be morphologically diverse in a variety of highly visual mammals in which feedforward visual pathways are organized into parallel processing streams. Accordingly, these results provide support for the notion that CG feedback is functionally parallel stream-specific in ferrets and macaques.

Theta Oscillations Organize Spiking Activity in Higher-Order Visual Thalamus during Sustained Attention

Higher-order visual thalamus plays a fundamental but poorly understood role in attention-demanding tasks. To investigate how neuronal dynamics in higher-order visual thalamus are modulated by sustained attention, we performed multichannel electrophysiological recordings in the lateral posterior-pulvinar complex (LP/pulvinar) in the ferret (Mustela putorius furo). We recorded single unit activity and local field potential (LFP) during the performance of the five-choice serial reaction time task (5-CSRTT), which is used in both humans and animals as an assay of sustained attention. We found that half of the units exhibited an increasing firing rate during the delay period before stimulus onset (attention-modulated units). In contrast, the non-attention-modulated units responded to the stimulus, but not during the delay period. Spike-field coherence (SFC) of only the attention-modulated neurons significantly increased from the start of the delay period until screen touch, predominantly in the θ frequency band. In addition, θ power and θ/γ phase amplitude coupling (PAC) were elevated throughout the delay period. Our findings suggest that the θ oscillation plays a central role in orchestrating thalamic signaling during sustained attention.

Resting state network topology of the ferret brain

Resting state functional magnetic resonance imaging (rsfMRI) has emerged as a versatile tool for non-invasive measurement of functional connectivity patterns in the brain. RsfMRI brain dynamics in rodents, non-human primates, and humans share similar properties; however, little is known about the resting state functional connectivity patterns in the ferret, an animal model with high potential for developmental and cognitive translational study. To address this knowledge-gap, we performed rsfMRI on anesthetized ferrets using a 9.4T MRI scanner, and subsequently performed group-level independent component analysis (gICA) to identify functionally connected brain networks. Group-level ICA analysis revealed distributed sensory, motor, and higher-order networks in the ferret brain. Subsequent connectivity analysis showed interconnected higher-order networks that constituted a putative default mode network (DMN), a network that exhibits altered connectivity in neuropsychiatric disorders. Finally, we assessed ferret brain topological efficiency using graph theory analysis and found that the ferret brain exhibits small-world properties. Overall, these results provide additional evidence for pan-species resting-state networks, further supporting ferret-based studies of sensory and cognitive function.