Reading Notes: Brain Timing and Function PDF

02 February 2024 21:51 Source Notes The brain timewise: how timing shapes and supports brain function (Hari and Parkkonen, 2015) 1. Introduction: Key Points: 1. Timing is crucial for brain function. 2. Traditional imaging (fMRI, PET) focuses on spatial aspects but is slow. 3. Shift towards understan...

02 February 2024 21:51 Source Notes The brain timewise: how timing shapes and supports brain function (Hari and Parkkonen, 2015) 1. Introduction: Key Points: 1. Timing is crucial for brain function. 2. Traditional imaging (fMRI, PET) focuses on spatial aspects but is slow. 3. Shift towards understanding dynamic brain networks. 4. MEG and EEG offer temporal precision but are less used. 5. The paper aims to explore temporal aspects of brain function, including in disorders and through new imaging techniques. Critical Analysis: ○ The emphasis on timing in brain function challenges the dominant focus on spatial aspects in brain imaging. It raises questions about how comprehensive our understanding of brain activity is when temporal dynamics are not the central focus. 2. Multiple Time Scales in Brain Function: Key Points: 1. Human brain operates on multiple time scales, from microseconds to years. 2. Temporal dynamics vary across functions: perception, action, cognition. 3. Different tasks require different timing accuracy and error tolerance. 4. The brain makes predictions with millisecond precision. 5. Time scales are nested and organized hierarchically. Critical Analysis: ○ This section highlights the complexity of the brain's temporal dynamics. The hierarchical organization of time scales suggests a sophisticated mechanism for processing and reacting to stimuli. Understanding these dynamics is crucial for unravelling brain function and dysfunction. 3. Temporal Constraints for Brain Architecture: Key Points: 1. Evolution shaped the brain's temporal dynamics for survival and communication. 2. The brain has adapted to environmental time scales. 3. Inverse relationship between the size of organisms and speed of passive responses. 4. Evolution led to coexistence of small spatial scales and short temporal windows in the brain. 5. Brain rhythms are preserved across species, indicating fundamental biophysical properties. Critical Analysis: ○ This section underscores how evolutionary pressures have shaped the brain's temporal architecture. The preservation of brain rhythms across species suggests fundamental constraints and principles guiding brain function. PSYC0031 Cognitive Neuroscience Page 1 constraints and principles guiding brain function. 4. Time-resolved Brain Imaging: Key Points: 1. MEG and EEG are non-invasive methods for recording neuronal activity. 2. These methods excel in capturing transient and rhythmic brain activity. 3. Challenges include recording slowly changing signals and the mixture of neural sources. 4. Spatial resolution limitations can lead to spurious functional connectivity findings. 5. MEG and EEG provide insights into low-level visual features and network structure. Critical Analysis: ○ The potential of MEG and EEG in capturing real-time brain activity is significant. However, their limitations in spatial resolution and difficulties in handling slow signals highlight the need for improved techniques or complementary methods. 5. Limitations of Current MEG/EEG Approaches: Key Points: 1. Bias towards responses from fastest fibres. 2. Difficulty in specifying temporal boundaries in brain hierarchy. 3. Challenges in discerning directional communication between brain areas. 4. Discrepancies between MEG/EEG and fMRI findings in cognitive tasks. Critical Analysis: ○ The limitations of MEG and EEG, particularly in detecting slower signalling processes and in establishing causality in brain networks, underscore the challenges in fully understanding the brain's temporal dynamics. 6. Future Methods for Non-invasive Time-resolved Brain Imaging: Key Points: 1. Proposed improvements include sensors closer to the cortex and increased spatial resolution. 2. Potential of optically pumped magnetometers (OPMs) in MEG. 3. Challenges in detecting deep brain structures and action potentials. 4. Importance of modeling and integration of multi-level data for understanding brain activity. Critical Analysis: ○ This section offers hope for future advancements in brain imaging technology. Innovations like OPMs could revolutionize our ability to study brain function in real-time, although challenges in detecting deep brain structures and action potentials remain. 7. Brains in Interaction: Key Points: 1. Critique of current brain imaging as 'spectator science.' 2. Proposal for 'two-person neuroscience' (2PN) to study social interactions. 3. 2PN aims to differentiate interactive vs. reactive states in social interactions. PSYC0031 Cognitive Neuroscience Page 2 social interactions. 4. Challenges in analysing 2PN data and the need for combined behavioral and body-state parameters. Critical Analysis: ○ The concept of 2PN challenges the traditional paradigm of brain imaging, emphasizing the importance of studying brains in interactive, social contexts. This approach could yield insights into the complexities of human social behavior and its neural underpinnings. 8. Timewise Insights into Brain Function: Key Points: 1. Importance of understanding the dynome (dynamic structure) of the brain. 2. The future might bring more detailed and complex models of brain function. 3. Potential of integrating neurophysiological data at different scales. 4. Challenges in data interpretation and the need for a theoretical framework. 5. Prediction of future research directions and methodologies in brain science. Critical Analysis: ○ This final section highlights the shift towards a more dynamic understanding of brain function. The focus on the dynome and chronnectome suggests a future where temporal dynamics are central to understanding the brain. However, the complexity of this data and the need for sophisticated models present significant challenges. Magnetoenceph alography: Applications in Psychiatry (Reite et al., 1999) 1. Introduction The first human MEG was reported by David Cohen in 1968. Cohen's work laid the foundation for using MEG to study alpha frequency magnetic fields. The technology has evolved significantly since, particularly in its application to mental illnesses. 2. Magnetic Fields of the Brain MEG measures magnetic fields produced by neuronal activity. About 50,000 similarly oriented cortical cells are needed to produce detectable magnetic fields. The technology focuses on intraneuronal currents, which are different from the extraneuronal currents detected by EEG. 3. Instrumentation MEG signals are typically in the range of 50-500 femto Tesla. Modern MEG instruments use superconducting technology and require liquid helium. MEG systems must often operate in magnetically shielded rooms to exclude external magnetic noise. The evolution of MEG technology has led to whole-head systems capable of monitoring over 100 channels. 4. MEG and Magnetic-Evoked Fields MEG recordings resemble EEG but with greater spatial localization capability. MEG can detect rhythms like alpha, mu, and tau, which reflect the "idling" state of primary sensory cortex. Its ability to identify these rhythms with precision surpasses that of EEG. MEG can also record during sleep, showing waveforms similar PSYC0031 Cognitive Neuroscience Page 3 MEG can also record during sleep, showing waveforms similar to those in sleep EEG. 5. MEG: Applications in Psychiatry MEG has provided insights into cerebral lateralization in psychoses. It has been instrumental in studying auditory sensory memory and potential cortical reorganization in schizophrenia. MEG's ability to capture high-frequency cortical activity aids in understanding gamma band-like phenomena and very highfrequency activity related to inhibitory interneuronal activity. 6. Disturbances in Cerebral Lateralization in Psychosis MEG has demonstrated anomalies in cerebral lateralization in auditory and somatosensory domains in psychoses. These disturbances in lateralization suggest developmental abnormalities in brain organization in psychotic disorders. 7. Auditory System in Schizophrenia MEG studies show anomalies in the auditory M100 component in schizophrenia. These anomalies may indicate altered echoic memory function and gender-related differences in brain organization in schizophrenia. 8. Studies of Somatosensory System in Affective Psychoses MEG has revealed altered lateralization in the somatosensory system in patients with affective psychoses. This suggests potential structural differences in the postcentral gyrus in these disorders. 9. Evidence for Possible Cortical Reorganization in Schizophrenia MEG findings indicate possible functional reorganization of the auditory cortex in schizophrenia. This implies shifts in cortical processing regions, challenging traditional understanding of brain organization in schizophrenia. 10. Gamma Band Generators and the P50 MEG studies have explored the role of gamma band activity in the P50 component in schizophrenia. This research suggests that disturbances in gamma band activity may underlie some symptoms of schizophrenia. 11. Summary MEG is a developing field with high potential despite its complexity and cost. It offers superior temporal and spatial resolution compared to EEG. MEG studies have contributed significantly to understanding cortical reorganization, memory function, and brain lateralization in psychotic disorders. Critical Analysis: The development and application of MEG represent a significant advancement in neuroimaging, especially in psychiatry. MEG's sensitivity to intraneuronal currents offers unique insights into PSYC0031 Cognitive Neuroscience Page 4 MEG's sensitivity to intraneuronal currents offers unique insights into brain function that are not accessible through other imaging techniques. The findings in schizophrenia and affective psychoses suggest that MEG could be crucial in understanding the neurobiological basis of these disorders. However, the complexity and cost of MEG limit its widespread use and underscore the need for more accessible technologies with similar capabilities. Future integration of MEG with other imaging techniques could enhance our understanding of brain disorders and aid in the development of more targeted treatments. FUNDAMENTAL S OF EEG MEASUREMEN T (Teplan, 2002) 1. Introduction to EEG: EEG is a non-invasive method for studying brain activity. It measures electrical activity from the scalp, reflecting brain function. Key for understanding neurological conditions and cognitive processes. 2. History of EEG: Traces back to the 19th century. Hans Berger was instrumental in its development. EEG has evolved to explore various brain states and disorders. 3. Brain Waves Classification: Brain waves are categorized into beta, alpha, theta, and delta waves. These categories reflect different brain states, such as alertness or relaxation. Alpha waves are particularly significant, often observed in the posterior regions. 4. Applications of EEG: Used in neurology and clinical neurophysiology. Important for diagnosing and understanding conditions like epilepsy, sleep disorders. Also used in cognitive research, including studies on perception and memory. 5. EEG Recording Techniques: Involves electrodes, amplifiers, filters, and recording devices. The quality of recording is crucial, influenced by electrode placement and equipment. Modern EEG involves sophisticated technology for accurate data collection. 6. Evoked Potentials in EEG: Evoked potentials are voltage fluctuations in response to stimuli. Useful for studying cognitive processes and neurological disorders. Provide insights into the timing and sequence of brain activities. 7. Quantitative Electroencephalography (QEEG): Enhances EEG data interpretation through advanced analysis. Allows for more precise spatial localization of brain activity. Used for brain mapping and understanding complex brain functions. PSYC0031 Cognitive Neuroscience Page 5 8. Brain-Computer Interface (BCI): BCI systems use EEG signals to control external devices. Reflects advancements in interpreting brain waves for practical applications. 9. EEG Biofeedback: Involves training individuals to alter their brain activity. Used for various therapeutic purposes, like treating ADHD or epilepsy. 10. Conclusion: EEG is a crucial tool in medical and research fields. Offers insights into brain function and disorders. Continues to evolve with technological advancements. Critical Analysis: EEG's evolution highlights its increasing relevance in medical diagnostics and cognitive research. While offering valuable temporal resolution, its spatial resolution is limited compared to other imaging techniques like MRI. The categorization of brain waves provides a fundamental framework for interpreting EEG data in various contexts. The development of QEEG and BCI reflects the growing sophistication in EEG analysis and its potential in real-world applications. EEG biofeedback opens new therapeutic avenues, demonstrating the practical implications of EEG research. Event-related brain potentials: an introduction (Rugg & Coles, 1996) 1. Introduction to ERPs and Cognitive Psychology: Focuses on the intersection of ERPs and cognitive psychology. Aims to understand human cognitive function through the electrical activity of the brain recorded via scalp electrodes. 2. ERP Recording and Analysis: Reviews basic concepts pertinent to ERP recording and analysis. Involves attaching electrodes to the scalp and connecting to an amplifier, revealing patterns of voltage variation known as the electroencephalogram (EEG). 3. Recording Issues in ERP: Discusses the common practice of using a 'common reference' recording procedure. Electrode locations are described with reference to the 10–20 system, which specifies electrode placement relative to brain regions. Highlights that brain activity detected at a scalp site may not directly correlate with activity in nearby brain regions due to the brain's volume conducting properties. 4. Filtering in ERP Recording: Amplifiers used for ERP recording usually have filter settings to attenuate irrelevant frequencies. High-frequency activities like muscle movements and line frequency noises are filtered out. Low-frequency activities can also be attenuated but require careful filtering to avoid distorting the ERP waveform. 5. Dealing with Artifacts in ERP Data: PSYC0031 Cognitive Neuroscience Page 6 5. Dealing with Artifacts in ERP Data: Filtering procedures can attenuate non-brain activity artifacts. Eye movements and blinks are major sources of artifacts, as they produce fluctuating electrical fields detected by scalp electrodes. Different methods are used to handle these artifacts, including instructing subjects to maintain gaze and avoid blinking at specific times. 6. Extracting the ERP Signal: The ERP is a set of voltage changes within an EEG epoch, timelocked to an event. Commonly, signal processing techniques like averaging are used to extract the ERP from background EEG noise. This technique involves averaging EEG values across multiple time-locked epochs to isolate the time-locked activity. 7. Limitations of Averaging Technique: Averaging cannot provide a direct estimate of the ERP elicited by individual events. The average waveform may not accurately represent actual waveforms recorded on individual trials. This can lead to challenges in interpreting differences in averaged waveforms, especially when the amplitude or latency of waveform features vary across trials. 8. Defining and Measuring ERP Components: A central issue in ERP research is defining what constitutes an ERP component. Components are usually identified by focusing on specific waveform features like peaks or troughs. However, this approach can be problematic due to component overlap and the volume conduction property of the brain. 9. Psychological Approaches to Component Identification: Involves selecting specific waveform features associated with particular psychological processes. One method is to subtract waveforms from different experimental conditions to isolate components linked to cognitive processes that differ between conditions. Critical Analysis: The document provides a thorough overview of ERP methodology, highlighting its potential and challenges in studying cognitive functions. The complexity of accurately recording and analysing ERPs, particularly concerning electrode placement and filtering, underscores the need for meticulous experimental design. Issues like artifact management and component overlap demonstrate the challenges in ensuring the data's reliability and interpretability. The use of averaging and subtraction methods for signal extraction and component identification, while useful, also presents limitations, indicating that ERP analysis requires careful consideration of various factors that might influence the results. Overall, the document illustrates the intricate nature of ERP research and its significance in advancing our understanding of cognitive processes through electrophysiological measures. PSYC0031 Cognitive Neuroscience Page 7