How do memories transfer?

This 2018 Chinese study electronically modeled the brain’s circuits to evaluate memory transfer mechanisms:

“During non-rapid-eye-movement (NREM) sleep, thalamo-cortical spindles and hippocampal sharp wave-ripples have been implicated in declarative memory consolidation. Evidence suggests that long-term memory consolidation is coordinated by the generation of:

  • Hierarchically nested hippocampal ripples (100-250 Hz),
  • Thalamo-cortical spindles (7-15 Hz), and
  • Cortical slow oscillations (<1 Hz)

enabling memory transfer from the hippocampus to the cortex.

Consolidation has also been demonstrated in other brain tasks, such as:

  • In the acquisition of motor skills, where there is a shift from activity in prefrontal cortex to premotor, posterior parietal, and cerebellar structures; and
  • In the transfer of conscious to unconscious tasks, where activity in initial unskilled tasks and activity in skilled performance are located in different regions, the so-called ‘scaffolding-storage’ framework.

By separating a neural circuit into a feedforward chain of gating populations and a second chain coupled to the gating chain (graded chain), graded information (i.e. information encoded in firing rate amplitudes) may be faithfully propagated and processed as it flows through the circuit. The neural populations in the gating chain generate pulses, which push populations in the graded chain above threshold, thus allowing information to flow in the graded chain.

In this paper, we will describe how a set of previously learned synapses may in turn be copied to another module with a pulse-gated transmission paradigm that operates internally to the circuit and is independent of the learning process.”


The study has neither been peer-reviewed, nor have the mechanisms been tested in living beings.

https://www.biorxiv.org/content/early/2018/07/27/351114 “A Mechanism for Synaptic Copy between Neural Circuits”

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Epigenetic effects of breast cancer treatments

This 2018 UC San Diego review subject was the interplay between breast cancer treatments and their effects on aging:

“Although current breast cancer treatments are largely successful in producing cancer remission and extending lifespan, there is concern that these treatments may have long lasting detrimental effects on cancer survivors, in part, through their impact on non-tumor cells. It is unclear whether breast cancer and/or its treatments are associated with an accelerated aging phenotype.

In this review, we have highlighted five of nine previously described cellular hallmarks of aging that have been described in the context of cytotoxic breast cancer treatments:

  1. Telomere attrition;
  2. Mitochondrial dysfunction;
  3. Genomic instability;
  4. Epigenetic alterations; and
  5. Cellular senescence.”


The review was full of caveats weakening the above graphic’s associations. To their credit, these reviewers at least presented some of the contrary evidence, and didn’t continue on with a directed narrative as many other reviewers are prone to do:

  1. “Telomere attrition – Blood TL [telomere length] was not associated with chemotherapy in three out of four studies;
  2. Mitochondrial dysfunction – How cancer therapies affect cellular energetics as they relate to rate of aging is unclear;
  3. Genomic instability – Potentially contributing to accelerated aging;
  4. Epigenetic alterations – Although some of the key regulators of these processes have begun to be identified, including DNA and histone methylases and demethylases, histone acetylases and de-acetylases and chromatin remodelers, how they regulate the changes in aging through alteration of global transcriptional programs, remains to be elucidated; and
  5. Cellular senescence – Dysregulated pathways can be targeted by cytotoxic chemotherapies, resulting in preferential cell death of tumor cells, but how these treatments also affect normal cells with intact pathways is unclear.”

https://www.sciencedirect.com/science/article/pii/S1879406818301176 “Breast cancer treatment and its effects on aging” (not freely available)


The originator of the epigenetic clock methodology was a coauthor of the review. Only one of his works was cited in the Epigenetic alterations subsection:

https://link.springer.com/article/10.1007%2Fs10549-017-4218-4 “DNA methylation age is elevated in breast tissue of healthy women”

This freely-available 2017 study quoted below highlighted that epigenetic clock measurements as originally designed were tissue-specific:

“To our knowledge, this is the first study to demonstrate that breast tissue epigenetic age exceeds that of blood tissue in healthy female donors. In addition to validating our earlier finding of age elevation in breast tissue, we further demonstrate that the magnitude of the difference between epigenetic age of breast and blood is highest in the youngest women in our study (age 20–30 years) and gradually diminishes with advancing age. As women approach the age of the menopausal transition, we found that the epigenetic of age of blood approaches that of the breast.”

Additional caution was justified in both interpreting age measurements and extending them into “cellular hallmarks” when the tissue contained varying cell types:

“Our studies were performed on whole breast tissue. Diverse types of cells make up whole breast tissue, with the majority of cells being adipocytes. Other types of cells include epithelial cells, cuboidal cells, myoepithelial cells, fibroblasts, inflammatory cells, vascular endothelial cells, preadipocytes, and adipose tissue macrophages.

This raises the possibility that the magnitude of the effects we observe, of breast tissue DNAm age being greater than other tissues, might be an underestimation, since it is possible that not all of the cells of the heterogenous sample have experienced this effect. Since it is difficult to extract DNA from adipose tissue, we suspect that the majority of DNA extracted from our whole breast tissues was from epithelial and myoepithelial cells.”

Allergies and epigenetic histone modifications

This 2018 German review provided short summaries of 44 studies on the contribution of histone modifications to allergies. An overall summary of their search results was:

“There are at least two levels at which the role of histone modifications is manifested.

  • One is the regulation of cells that contribute to the allergic inflammation (T cells and macrophages) and those that participate in airway remodeling.
  • The other is the direct association between histone modifications and allergic phenotypes.

Inhibitors of histone-modifying enzymes may potentially be used as anti-allergic drugs. Furthermore, epigenetic patterns may provide novel tools in the diagnosis of allergic disorders.”


This type of search is what’s expected of researchers who will perform either:

  • A meta-analysis of studies selected from the search results; or
  • Their own study.

These reviewers didn’t indicate that they were proceeding along either path.

The review was fine for the purpose of presenting current studies of the subject. But the review was just the preparatory stage of research.

https://aacijournal.biomedcentral.com/articles/10.1186/s13223-018-0259-4 “Histone modifications and their role in epigenetics of atopy and allergic diseases”

Epigenetic variations in metabolism

This 2018 German review was comprehensive for its subject, epigenetic control of variation and stochasticity in metabolic disease. I’ll focus on one aspect, phenotypic variation:

“Phenotypic [Mendelian] variation can result both from gain- and loss-of-function mutations. Because of the extreme interconnectivity of cell regulatory networks, even at the cellular level, predicting the impact of a sequence variant is difficult as the resultant variation acts:

  • In the context of all other variants and
  • Their potential additive, synergistic and antagonistic interactions.

This phenomenon is known as epistasis.

∼98.5% of our genome is non-protein-coding: it is pervasively transcribed, and its transcripts can support regulatory function. Among the best functionally characterized non-coding RNAs (ncRNAs) arising from these sequences are microRNAs (miRNAs)

Environmental [non-Mendelian] variation or ‘stimuli’ occurring during critical windows of susceptibility can elicit lifelong alterations in an individual’s phenotype. Intergenerational metabolic reprogramming [in fruit flies] results from global alterations in chromatin state integrity, particularly from reduced H3K27me3 and H3K9me3 [histone] domains.

The broad variation of fingerprints in humans is thought to depend to a large degree on stochastic variation in mechanical forces. These clear examples of inducible multi-stable or stochastic variation highlight how little we know about the landscape of potential phenotypic variation itself.

Consensus estimates of heritability for obesity and T2D are ∼70% and ∼35% respectively. The remaining, unexplained component is known to involve gene–environment interactions as well as non-Mendelian players.”


Although the above graphic displays transgenerational inheritance for humans, the reviewers didn’t cite any human studies that adequately demonstrated causes for and effects of transgenerational epigenetic inheritance.

I’ve read the cited Swedish and Dutch studies. Their designs, methods, and “correlate with” / “was associated with” results didn’t provide incontrovertible evidence from the F0 great-grandparents, F1 grandparents, F2 parents, and F3 children. It’s necessary to thoroughly study each generation to confirm definitive transgenerational epigenetic inheritance causes and effects.

As noted in How to hijack science: Ignore its intent and focus on the 0.0001%, there aren’t any such published studies to cite. Researchers urgently need to do this human research, and stop using these poor substitutes [1] to pretend there are already adequately evidenced transgenerational epigenetic inheritance human results.

I downgraded the review for treating research of this and other subjects as faits accomplis. It’s opposite ends of the evidential spectrum to state “how little we know about the landscape of potential phenotypic variation,” and in the same review, speciously extrapolate animal experiments into putative human results.

https://www.sciencedirect.com/science/article/pii/S2212877818301984 “Epigenetic control of variation and stochasticity in metabolic disease”


[1] As an example of the poor substitutes for evidence, a researcher referred me to the 2013 “Transgenerational effects of prenatal exposure to the 1944–45 Dutch famine” which is freely available at https://obgyn.onlinelibrary.wiley.com/doi/full/10.1111/1471-0528.12136 as a study finding human transgenerational epigenetic inheritance.

The methods section showed:

  • The study’s non-statistical data was almost all self-reported by a self-selected sample of the F2 grandchildren, average age 37.
  • No detailed physical measurements or samples were taken of them, or of their F1 parents, or of their F0 grandparents, all of which are required as baselines for any transgenerational epigenetic inheritance.
  • No detailed physical measurements or samples were taken of their F3 children, which is the generation that may provide evidence for transgenerational findings if the previous generations also have detailed physical baselines.

The study’s researchers drew enough participants (360) such that their statistics package allowed them to impute and assume into existence a LOT of data. But the scientific method constrained them to make factual statements of what the evidence actually showed. They admitted:

“In conclusion, we did not find a transgenerational effect of prenatal famine exposure on the health of grandchildren in this study.”

Yet this study is somehow cited for evidence of human transgenerational epigenetically inherited causes and effects.

Group statistics don’t necessarily describe an individual

I’m curating this 2018 UC Berkeley/Drexel/Netherlands analysis of human studies via its press coverage. The authors:

“Collaborated to analyze data on hundreds of adults – some mentally or physically sound, others suffering from various conditions such as depression, anxiety, or post-traumatic stress disorder. Participants had completed surveys about their mental health and had their heart rates monitored via electrocardiogram.

Researchers used the data to conduct six different experiments. They sought to find out whether the conclusions of each study would successfully apply to participants individually.

One study that focused on how frequently depression sufferers reported feeling worried. Results tallied from the pool of participants showed that depressed people worry a significant amount.

But when the analysis was applied individually, the results were all over the map. Some participants worried hardly at all, while others were notably beyond the group average.

Another experiment that centered around the link between fear and avoidance showed a strong correlation when measured as a group. Yet a significant number of participants who experienced fear had no issues with avoiding various activities.

Across all six experiments, the authors could not show that what was concluded for the group applied to most individuals.”


Other studies such as the below have addressed problems with statistical analysis techniques. The issues aren’t limited to human studies:

The current study highlighted the fact that people aren’t interchangeable. Assuming ergodicity is a statistical analysis flaw that produces individually inapplicable results for many measurements of fruit flies, cells, humans, you name the organism.

When this presumption makes a study’s statistics useless for an individual, researchers can’t cure the analysis by invoking an “individual differences” meme. Neither is the flaw fixed by spinning a tale about “This is how we can truly personalize medicine.” The current study needed to provide evidence for its proposed solution.


Regarding worrying, Dr. Arthur Janov said it best as I quoted in How well can catastrophes be predicted?:

“Worrying is not a problem, it is the symptom of something that is occurring physiologically within the brain. What causes the worrying is the problem.

The constant worry is anticipating catastrophe. But what we don’t realize is that the catastrophe already has happened; we simply have no access to it.

We are actually worried about the past, not the future.”

http://www.pnas.org/content/early/2018/06/15/1711978115 “Lack of group-to-individual generalizability is a threat to human subjects research” (not freely available)

The hypothalamus and aging

This 2018 Korean review discussed aspects of the hypothalamus and aging:

“A majority of physiological functions that decline with aging are broadly governed by the hypothalamus, a brain region controlling development, metabolism, reproduction, circadian rhythm, and homeostasis. In addition, the hypothalamus is poised to connect the brain and the body so that the environmental information affecting aging can be transmitted through the hypothalamus to affect the systematic aging of the peripheral organs.

The hypothalamus is hypothesized to be a primary regulator of the process of aging of the entire body. This review aims to assess the contribution of hypothalamic aging to the age-related decline in body functions, particularly from the perspective of:

  • energy homeostasis,
  • hormonal balance,
  • circadian rhythm, and
  • reproduction,

and to highlight its underlying cellular mechanisms with a focus on:

  • nutrient sensing
  • inflammation,
  • loss of stem cell,
  • loss of proteostasis, and
  • epigenetic alterations.”


The reviewers didn’t consider aging to be an “unintended consequence” of development. This perspective was found in a reference to A study of DNA methylation and age:

“Aging is not and cannot be programmed. Instead, aging is a continuation of developmental growth, driven by genetic pathways.

Genetic programs determine developmental growth and the onset of reproduction. When these programs are completed, they are not switched off.

Aging has no purpose (neither for individuals nor for group), no intention. Nature does not select for quasi-programs. It selects for robust developmental growth.”

The epigenetic clock theory of aging cited the same author, and modified his point to say:

“The proposed epigenetic clock theory of ageing views biological ageing as an unintended consequence of both developmental programmes and maintenance programmes.”

This review’s opposite paradigm was:

“The hypothalamus is hypothesized to be a primary regulator of the process of aging.”

Almost all of the details discussed were from rodent studies.


I favor the “unintended consequence” explanation of aging. As detailed in How to cure the ultimate causes of migraines? and its references, the hypothalamus is a brain structure that lacks feedback mechanisms for several of its activities.

This structure develops shortly after conception and has an active prenatal role. The hypothalamus plays its part in getting us developed and ready to reproduce, with several feedback loops being evolutionarily unnecessary.

The hypothalamus perfectly illustrates the point of:

“When these programs are completed, they are not switched off.”

Should hypothalamic activity not winding down when its developmental role is over be interpreted to construe a role that has some other meaning or purpose as we age?

https://www.sciencedirect.com/science/article/pii/S0047637418300502 “Role of hypothalamus in aging and its underlying cellular mechanisms” (not freely available)

An evolutionary view of stress and cancer

This 2018 Michigan review subject was cancer evolution:

“Based on the fact that cancer typically represents a complex adaptive system, where there is no linear relationship between lower-level agents (such as each individual gene mutation) and emergent properties (such as cancer phenotypes), we call for a new strategy based on the evolutionary mechanism of aneuploidy [abnormal number of chromosomes] in cancer, rather than continuous analysis of various individual molecular mechanisms.

Cancer evolution can be understood by the dynamic interaction among four key components:

  1. Internal and external stress;
  2. Elevated genetic and non-genetic variations (either necessary for cellular adaptation or resulting from cellular damages under stress);
  3. Genome-based macro-cellular evolution (genome replacement, emergent as new systems); and
  4. Multiple levels of system constraint which prevent/slow down cancer evolution (from tissue/organ organization to the immune system interaction).

Since the sources of stress are unlimited and unavoidable (as they are required by all living systems), there are large numbers of gene mutations / epigenetic events / chromosomal aberrations, such as aneuploidy, that can be linked to stress-mediated genomic variants. Furthermore, as environmental constraints are constantly changing, even identical instances of aneuploidy will have completely different outcomes in the context of cancer evolution, as the results of each independent run of evolution will most likely differ.

Most current research efforts are focusing on molecular profiles based on an average population, and outliers are eliminated or ignored, either by the methods used or statistical tools. The traditional view of biological research is to identify patterns from “noise,” without the realization that the so-called “noise” in fact is heterogeneity, which represents a key feature of cancer evolution by functioning as the evolutionary potential.

Understanding the molecular mechanism (both cause and effect) of aneuploidy is far from enough. A better strategy is to monitor the evolutionary process by measuring evolutionary potential. For example, the overall degree of CIN [chromosome instability] is more predictive than individual gene mutation profile.”


Although I read many abstracts of cancer research papers every week, I usually don’t curate them. I curated this paper because the reviewers emphasized several themes of this blog, including:

  • Further examples of how stress may shape one’s life.
  • How researchers miss information when they ignore or process away variation:

    Studies have demonstrated the importance of outliers in cancer evolution, as cancer is an evolutionary game of outliers. While this phenomenon can provide a potential advantage for cellular adaptation, it can also, paradoxically, generate non-specific system stress, which can further produce more genetic and non-genetic variants which favor the disease condition.”

Epigenetics researchers may benefit from evolutionary viewpoints that incorporate the interactions of stress and “genetic and non-genetic variants.”

Since epigenetic changes require inheritance in order to persist, it would be a step forward to see researchers start “measuring evolutionary potential” of these inheritance processes.

https://molecularcytogenetics.biomedcentral.com/articles/10.1186/s13039-018-0376-2 “Understanding aneuploidy in cancer through the lens of system inheritance, fuzzy inheritance and emergence of new genome systems”