See the article here: A default mode of brain function: A brief history of an evolving idea Marcus E. Raichlea,b,c,⁎ and Abraham Z. Snydera,b

Disclaimer: The content below was generated with the assistance of AI and then reviewed and edited by BrainMaster Technologies, Inc. It is provided for educational and informational purposes only and does not constitute medical advice.

Introduction #

The article by Marcus E. Raichle and Abraham Z. Snyder presents a historical and conceptual overview of the discovery and evolution of the brain’s Default Mode of Function (DMF)—a foundational concept for understanding intrinsic neural activity. Using PET and fMRI evidence, the authors describe how consistent task-related decreases in several brain regions led to the identification of the Default Mode Network (DMN).

Origins of the Default Mode Concept #

Observations in Early Neuroimaging #

Early PET studies revealed consistent activity decreases during task performance compared with passive states such as visual fixation or eyes-closed rest. These decreases involved regions such as the posterior cingulate cortex and precuneus, originally labeled the “medial mystery parietal area” (p. 1085).

Researchers questioned whether the “resting” baseline might contain active intrinsic processes, prompting re-examination of assumptions underlying functional neuroimaging.

Physiological Basis of the Default Mode #

PET Evidence for a Physiologic Baseline #

The authors used quantitative PET measures of oxygen extraction fraction (OEF) to test whether resting states contained covert activations. Findings showed that OEF remained uniform across the brain at rest (p. 1086), implying that task-related decreases reflected attenuation from an ongoing intrinsic process, not deactivation from a true idle state.

Energy Demands of Intrinsic Activity #

Intrinsic neural activity consumes 60–80% of the brain’s energy budget, compared with only ~0.5–1% for stimulus-evoked activity (p. 1087). This suggests that ongoing, spontaneous neural processes are functionally central, not background noise.

Functional Organization of Resting-State Activity #

Coherent Low-Frequency BOLD Fluctuations #

Resting-state fMRI reveals slow (<0.1 Hz) synchronized BOLD fluctuations that form coherent functional networks even without tasks (Fig. 1B–C, p. 1085). These patterns:

• Are reproducible across individuals

• Persist during sleep and general anesthesia

• Parallel known cognitive systems

This demonstrates that the brain maintains continuous intrinsic organization, independent of external demands.

Conceptual Interpretation of Intrinsic Activity #

Beyond Stimulus-Independent Thought #

Although intrinsic activity may include mind-wandering, the authors argue this cannot fully explain its high metabolic cost or its presence under anesthesia (p. 1087). Instead, intrinsic activity reflects fundamental operational properties of the brain.

The Brain as a Predictive System #

The article connects intrinsic activity to Bayesian inference models, proposing that the brain continuously maintains and updates internal representations (“priors”), enabling efficient responses and future-oriented prediction (p. 1088).

Implications for Cognitive Neuroscience #

The authors argue that studying intrinsic activity provides essential insights into:

• Variability in task-evoked responses

• The baseline context shaping all cognition

• The interplay between intrinsic and evoked activity

• Multilevel integration across neurophysiology, imaging, and behavior

They advocate for a research agenda that integrates resting-state analysis with traditional task-based neuroscience.

Key Takeaways #

• The Default Mode concept emerged from observing consistent task-induced decreases in specific brain regions.

• PET studies demonstrated that these regions are not activated at rest, but rather maintain sustained intrinsic activity that decreases during tasks.

• Intrinsic activity is energetically dominant, highly organized, and essential for brain function.

• Understanding intrinsic activity is critical for advancing neuroscience beyond stimulus-driven models.